diff --git a/qiskit/advanced/aqua/Aqua_how_to_build_a_pluggable_algorithm_components.ipynb b/qiskit/advanced/aqua/Aqua_how_to_build_a_pluggable_algorithm_components.ipynb
index f60e4118b..0e9d3fa86 100644
--- a/qiskit/advanced/aqua/Aqua_how_to_build_a_pluggable_algorithm_components.ipynb
+++ b/qiskit/advanced/aqua/Aqua_how_to_build_a_pluggable_algorithm_components.ipynb
@@ -1,25 +1,22 @@
 {
  "cells": [
   {
+   "attachments": {
+    "qiskit_header.png": {
+     "image/png": 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"
+    }
+   },
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<img src=\"../../../images/qiskit_header.png\" alt=\"Note: In order for images to show up in this jupyter notebook you need to select File => Trusted Notebook\" align=\"middle\">"
+    "![qiskit_header.png](attachment:qiskit_header.png)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# _*Qiskit Aqua: Tutorial to build a pluggable algorithm/component*_ \n",
-    "\n",
-    "The latest version of this notebook is available on https://github.com/Qiskit/qiskit-tutorial.\n",
-    "\n",
-    "***\n",
-    "### Contributors\n",
-    "Richard Chen<sup>[1]</sup>, Shaohan Hu<sup>[1]</sup>, Antonio Mezzacapo<sup>[1]</sup>, Peng Liu<sup>[1]</sup>, Marco Pistoia<sup>[1]</sup>, Stephen Wood<sup>[1]</sup>\n",
-    "### Affiliation\n",
-    "- <sup>[1]</sup>IBMQ"
+    "# Qiskit Aqua: Tutorial to build a pluggable algorithm/component\n"
    ]
   },
   {
@@ -55,23 +52,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 1,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2019-08-22T01:38:44.833048Z",
-     "start_time": "2019-08-22T01:38:40.295416Z"
+     "end_time": "2019-12-12T15:16:11.185336Z",
+     "start_time": "2019-12-12T15:16:08.136942Z"
     }
    },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Numpy 1.16 has memory leak bug  https://github.com/numpy/numpy/issues/13808\n",
-      "It is recommended to downgrade to numpy 1.15 or older\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from qiskit.aqua import register_pluggable\n",
     "from evolutionfidelity.evolutionfidelity import EvolutionFidelity\n",
@@ -83,11 +71,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 2,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2019-08-22T01:38:44.849045Z",
-     "start_time": "2019-08-22T01:38:44.845866Z"
+     "end_time": "2019-12-12T15:16:11.201460Z",
+     "start_time": "2019-12-12T15:16:11.198572Z"
     }
    },
    "outputs": [],
@@ -107,11 +95,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 3,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2019-08-22T01:38:44.865582Z",
-     "start_time": "2019-08-22T01:38:44.862191Z"
+     "end_time": "2019-12-12T15:16:11.217871Z",
+     "start_time": "2019-12-12T15:16:11.214681Z"
     }
    },
    "outputs": [
@@ -137,11 +125,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 4,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2019-08-22T01:38:45.795524Z",
-     "start_time": "2019-08-22T01:38:45.524414Z"
+     "end_time": "2019-12-12T15:16:11.402263Z",
+     "start_time": "2019-12-12T15:16:11.283486Z"
     }
    },
    "outputs": [
@@ -149,7 +137,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.9679647770258577\n"
+      "0.9420934576187111\n"
      ]
     }
    ],
@@ -182,11 +170,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 5,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2019-08-22T01:38:47.501168Z",
-     "start_time": "2019-08-22T01:38:47.495839Z"
+     "end_time": "2019-12-12T15:16:12.488671Z",
+     "start_time": "2019-12-12T15:16:12.483037Z"
     }
    },
    "outputs": [],
@@ -212,19 +200,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 6,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2019-08-22T01:38:51.687723Z",
-     "start_time": "2019-08-22T01:38:48.098825Z"
+     "end_time": "2019-12-12T15:16:24.808291Z",
+     "start_time": "2019-12-12T15:16:13.001536Z"
     }
    },
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/paul/opt/anaconda3/envs/qiskit/lib/python3.7/site-packages/qiskit/providers/models/backendconfiguration.py:337: UserWarning: `dt` and `dtm` now have units of seconds(s) rather than nanoseconds(ns).\n",
+      "  warnings.warn('`dt` and `dtm` now have units of seconds(s) rather '\n"
+     ]
+    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.9679647770258577\n"
+      "0.9420934576187111\n"
      ]
     }
    ],
@@ -235,19 +231,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 7,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2019-08-22T01:38:34.386128Z",
-     "start_time": "2019-08-22T01:38:34.375938Z"
+     "end_time": "2019-12-12T15:16:24.898534Z",
+     "start_time": "2019-12-12T15:16:24.823965Z"
     }
    },
    "outputs": [
     {
      "data": {
       "text/html": [
-       "<h3>Version Information</h3><table><tr><th>Qiskit Software</th><th>Version</th></tr><tr><td>Qiskit</td><td>None</td></tr><tr><td>Terra</td><td>0.9.0</td></tr><tr><td>Aer</td><td>0.3.0</td></tr><tr><td>Ignis</td><td>0.2.0</td></tr><tr><td>Aqua</td><td>0.5.6</td></tr><tr><td>IBM Q Provider</td><td>0.3.2rc1</td></tr><tr><th>System information</th></tr><tr><td>Python</td><td>3.7.4 (default, Aug 13 2019, 15:17:50) \n",
-       "[Clang 4.0.1 (tags/RELEASE_401/final)]</td></tr><tr><td>OS</td><td>Darwin</td></tr><tr><td>CPUs</td><td>4</td></tr><tr><td>Memory (Gb)</td><td>16.0</td></tr><tr><td colspan='2'>Wed Aug 21 21:38:34 2019 EDT</td></tr></table>"
+       "<h3>Version Information</h3><table><tr><th>Qiskit Software</th><th>Version</th></tr><tr><td>Qiskit</td><td>0.14.0</td></tr><tr><td>Terra</td><td>0.11.0</td></tr><tr><td>Aer</td><td>0.3.4</td></tr><tr><td>Ignis</td><td>0.2.0</td></tr><tr><td>Aqua</td><td>0.6.1</td></tr><tr><td>IBM Q Provider</td><td>0.4.4</td></tr><tr><th>System information</th></tr><tr><td>Python</td><td>3.7.5 (default, Oct 25 2019, 10:52:18) \n",
+       "[Clang 4.0.1 (tags/RELEASE_401/final)]</td></tr><tr><td>OS</td><td>Darwin</td></tr><tr><td>CPUs</td><td>4</td></tr><tr><td>Memory (Gb)</td><td>16.0</td></tr><tr><td colspan='2'>Thu Dec 12 10:16:24 2019 EST</td></tr></table>"
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -299,7 +295,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.4"
+   "version": "3.7.5"
   },
   "varInspector": {
    "cols": {
diff --git a/qiskit/advanced/aqua/amplitude_estimation.ipynb b/qiskit/advanced/aqua/amplitude_estimation.ipynb
index 9d84ac81d..b4a88d315 100644
--- a/qiskit/advanced/aqua/amplitude_estimation.ipynb
+++ b/qiskit/advanced/aqua/amplitude_estimation.ipynb
@@ -1,25 +1,22 @@
 {
  "cells": [
   {
+   "attachments": {
+    "qiskit_header.png": {
+     "image/png": 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"
+    }
+   },
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<img src=\"../../../images/qiskit_header.png\" alt=\"Note: In order for images to show up in this jupyter notebook you need to select File => Trusted Notebook\" align=\"middle\">"
+    "![qiskit_header.png](attachment:qiskit_header.png)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# _*Qiskit Aqua: Amplitude Estimation*_ \n",
-    "\n",
-    "The latest version of this notebook is available on https://github.com/Qiskit/qiskit-iqx-tutorials.\n",
-    "\n",
-    "***\n",
-    "### Contributors\n",
-    "Stefan Woerner<sup>[1]</sup>, Daniel Egger<sup>[1]</sup>, Shaohan Hu<sup>[1]</sup>, Stephen Wood<sup>[1]</sup>, Marco Pistoia<sup>[1]</sup>\n",
-    "### Affliation\n",
-    "- <sup>[1]</sup>IBMQ"
+    "# Qiskit Aqua: Amplitude Estimation\n"
    ]
   },
   {
@@ -46,11 +43,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 1,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2019-08-22T01:37:33.629914Z",
-     "start_time": "2019-08-22T01:37:33.622056Z"
+     "end_time": "2019-12-12T15:14:57.602059Z",
+     "start_time": "2019-12-12T15:14:53.913746Z"
     }
    },
    "outputs": [],
@@ -68,22 +65,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 2,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2019-08-22T01:37:34.320819Z",
-     "start_time": "2019-08-22T01:37:33.836392Z"
+     "end_time": "2019-12-12T15:14:58.627777Z",
+     "start_time": "2019-12-12T15:14:58.209290Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 360x360 with 1 Axes>"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 2,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -99,11 +96,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 3,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2019-08-22T01:37:34.363848Z",
-     "start_time": "2019-08-22T01:37:34.350108Z"
+     "end_time": "2019-12-12T15:15:00.298755Z",
+     "start_time": "2019-12-12T15:15:00.288182Z"
     }
    },
    "outputs": [],
@@ -151,11 +148,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 4,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2019-08-22T01:37:34.407466Z",
-     "start_time": "2019-08-22T01:37:34.404155Z"
+     "end_time": "2019-12-12T15:15:00.889272Z",
+     "start_time": "2019-12-12T15:15:00.886042Z"
     }
    },
    "outputs": [],
@@ -167,11 +164,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 5,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2019-08-22T01:37:34.454252Z",
-     "start_time": "2019-08-22T01:37:34.448965Z"
+     "end_time": "2019-12-12T15:15:01.277412Z",
+     "start_time": "2019-12-12T15:15:01.272919Z"
     }
    },
    "outputs": [],
@@ -187,11 +184,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 6,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2019-08-22T01:37:34.772976Z",
-     "start_time": "2019-08-22T01:37:34.668445Z"
+     "end_time": "2019-12-12T15:15:01.736613Z",
+     "start_time": "2019-12-12T15:15:01.653715Z"
     }
    },
    "outputs": [],
@@ -202,17 +199,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 7,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2019-08-22T01:37:35.461524Z",
-     "start_time": "2019-08-22T01:37:35.319533Z"
+     "end_time": "2019-12-12T15:15:02.180603Z",
+     "start_time": "2019-12-12T15:15:02.051886Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -238,22 +235,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 8,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2019-08-22T01:37:36.797118Z",
-     "start_time": "2019-08-22T01:37:36.280266Z"
+     "end_time": "2019-12-12T15:15:02.701855Z",
+     "start_time": "2019-12-12T15:15:02.195773Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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       "text/plain": [
        "<Figure size 1661.52x565.88 with 1 Axes>"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -265,19 +262,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 9,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2019-08-22T01:38:03.300743Z",
-     "start_time": "2019-08-22T01:38:03.292726Z"
+     "end_time": "2019-12-12T15:15:02.870756Z",
+     "start_time": "2019-12-12T15:15:02.784852Z"
     }
    },
    "outputs": [
     {
      "data": {
       "text/html": [
-       "<h3>Version Information</h3><table><tr><th>Qiskit Software</th><th>Version</th></tr><tr><td>Qiskit</td><td>None</td></tr><tr><td>Terra</td><td>0.9.0</td></tr><tr><td>Aer</td><td>0.3.0</td></tr><tr><td>Ignis</td><td>0.2.0</td></tr><tr><td>Aqua</td><td>0.5.6</td></tr><tr><td>IBM Q Provider</td><td>0.3.2rc1</td></tr><tr><th>System information</th></tr><tr><td>Python</td><td>3.7.4 (default, Aug 13 2019, 15:17:50) \n",
-       "[Clang 4.0.1 (tags/RELEASE_401/final)]</td></tr><tr><td>OS</td><td>Darwin</td></tr><tr><td>CPUs</td><td>4</td></tr><tr><td>Memory (Gb)</td><td>16.0</td></tr><tr><td colspan='2'>Wed Aug 21 21:38:03 2019 EDT</td></tr></table>"
+       "<h3>Version Information</h3><table><tr><th>Qiskit Software</th><th>Version</th></tr><tr><td>Qiskit</td><td>0.14.0</td></tr><tr><td>Terra</td><td>0.11.0</td></tr><tr><td>Aer</td><td>0.3.4</td></tr><tr><td>Ignis</td><td>0.2.0</td></tr><tr><td>Aqua</td><td>0.6.1</td></tr><tr><td>IBM Q Provider</td><td>0.4.4</td></tr><tr><th>System information</th></tr><tr><td>Python</td><td>3.7.5 (default, Oct 25 2019, 10:52:18) \n",
+       "[Clang 4.0.1 (tags/RELEASE_401/final)]</td></tr><tr><td>OS</td><td>Darwin</td></tr><tr><td>CPUs</td><td>4</td></tr><tr><td>Memory (Gb)</td><td>16.0</td></tr><tr><td colspan='2'>Thu Dec 12 10:15:02 2019 EST</td></tr></table>"
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -319,6 +316,18 @@
    "language": "python",
    "name": "python3"
   },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.5"
+  },
   "varInspector": {
    "cols": {
     "lenName": 16,
diff --git a/qiskit/advanced/aqua/artificial_intelligence/index.ipynb b/qiskit/advanced/aqua/artificial_intelligence/index.ipynb
index f815dcc60..dd6a7b329 100644
--- a/qiskit/advanced/aqua/artificial_intelligence/index.ipynb
+++ b/qiskit/advanced/aqua/artificial_intelligence/index.ipynb
@@ -1,10 +1,15 @@
 {
  "cells": [
   {
+   "attachments": {
+    "qiskit_header.png": {
+     "image/png": 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"
+    }
+   },
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<img src=\"../../../../images/qiskit_header.png\" alt=\"Note: In order for images to show up in this jupyter notebook you need to select File => Trusted Notebook\" align=\"middle\">"
+    "![qiskit_header.png](attachment:qiskit_header.png)"
    ]
   },
   {
@@ -46,7 +51,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.4"
+   "version": "3.7.5"
   },
   "varInspector": {
    "cols": {
diff --git a/qiskit/advanced/aqua/artificial_intelligence/qgans_for_loading_random_distributions.ipynb b/qiskit/advanced/aqua/artificial_intelligence/qgans_for_loading_random_distributions.ipynb
index 0c82153e1..4e684d534 100644
--- a/qiskit/advanced/aqua/artificial_intelligence/qgans_for_loading_random_distributions.ipynb
+++ b/qiskit/advanced/aqua/artificial_intelligence/qgans_for_loading_random_distributions.ipynb
@@ -1,26 +1,23 @@
 {
  "cells": [
   {
+   "attachments": {
+    "qiskit_header.png": {
+     "image/png": 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"
+    }
+   },
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<img src=\"../../../../images/qiskit_header.png\" alt=\"Note: In order for images to show up in this jupyter notebook you need to select File => Trusted Notebook\" align=\"middle\">"
+    "![qiskit_header.png](attachment:qiskit_header.png)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# _*Qiskit Aqua: qGANs for Loading Random Distributions*_ \n",
+    "# Qiskit Aqua: qGANs for Loading Random Distributions\n",
     "\n",
-    "The latest version of this notebook is available on https://github.com/Qiskit/qiskit-iqx-tutorials.\n",
-    "\n",
-    "***\n",
-    "### Contributors\n",
-    "Christa Zoufal<sup>[1,2]</sup>, Stefan Woerner<sup>[1]</sup>\n",
-    "### Affliation\n",
-    "- <sup>[1]</sup>IBMQ\n",
-    "- <sup>[2]</sup>ETH Zurich\n",
     "\n",
     "### Introduction\n",
     "Given $k$-dimensional data samples, we employ a quantum Generative Adversarial Network (qGAN) to learn the data's underlying random distribution and to load it directly into a quantum state:\n",
@@ -40,7 +37,12 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T15:35:19.415884Z",
+     "start_time": "2019-12-12T15:35:15.376156Z"
+    }
+   },
    "outputs": [],
    "source": [
     "import numpy as np\n",
@@ -81,7 +83,12 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T15:35:19.458659Z",
+     "start_time": "2019-12-12T15:35:19.454776Z"
+    }
+   },
    "outputs": [],
    "source": [
     "# Number training data samples\n",
@@ -114,7 +121,12 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T15:35:19.587348Z",
+     "start_time": "2019-12-12T15:35:19.553617Z"
+    }
+   },
    "outputs": [],
    "source": [
     "# Set number of training epochs\n",
@@ -171,13 +183,18 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T16:28:38.551126Z",
+     "start_time": "2019-12-12T15:35:19.686508Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "qGAN training runtime:  20.069843916098275  min\n"
+      "qGAN training runtime:  53.37901471853256  min\n"
      ]
     }
    ],
@@ -202,11 +219,16 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T16:28:39.266507Z",
+     "start_time": "2019-12-12T16:28:38.696529Z"
+    }
+   },
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x360 with 1 Axes>"
       ]
@@ -218,7 +240,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x360 with 1 Axes>"
       ]
@@ -230,7 +252,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 432x360 with 1 Axes>"
       ]
@@ -291,19 +313,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 6,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2019-08-22T01:49:13.439275Z",
-     "start_time": "2019-08-22T01:49:13.430311Z"
+     "end_time": "2019-12-12T16:28:39.530397Z",
+     "start_time": "2019-12-12T16:28:39.394147Z"
     }
    },
    "outputs": [
     {
      "data": {
       "text/html": [
-       "<h3>Version Information</h3><table><tr><th>Qiskit Software</th><th>Version</th></tr><tr><td>Qiskit</td><td>None</td></tr><tr><td>Terra</td><td>0.9.0</td></tr><tr><td>Aer</td><td>0.3.0</td></tr><tr><td>Ignis</td><td>0.2.0</td></tr><tr><td>Aqua</td><td>0.5.6</td></tr><tr><td>IBM Q Provider</td><td>0.3.2rc1</td></tr><tr><th>System information</th></tr><tr><td>Python</td><td>3.7.4 (default, Aug 13 2019, 15:17:50) \n",
-       "[Clang 4.0.1 (tags/RELEASE_401/final)]</td></tr><tr><td>OS</td><td>Darwin</td></tr><tr><td>CPUs</td><td>4</td></tr><tr><td>Memory (Gb)</td><td>16.0</td></tr><tr><td colspan='2'>Wed Aug 21 21:49:13 2019 EDT</td></tr></table>"
+       "<h3>Version Information</h3><table><tr><th>Qiskit Software</th><th>Version</th></tr><tr><td>Qiskit</td><td>0.14.0</td></tr><tr><td>Terra</td><td>0.11.0</td></tr><tr><td>Aer</td><td>0.3.4</td></tr><tr><td>Ignis</td><td>0.2.0</td></tr><tr><td>Aqua</td><td>0.6.1</td></tr><tr><td>IBM Q Provider</td><td>0.4.4</td></tr><tr><th>System information</th></tr><tr><td>Python</td><td>3.7.5 (default, Oct 25 2019, 10:52:18) \n",
+       "[Clang 4.0.1 (tags/RELEASE_401/final)]</td></tr><tr><td>OS</td><td>Darwin</td></tr><tr><td>CPUs</td><td>4</td></tr><tr><td>Memory (Gb)</td><td>16.0</td></tr><tr><td colspan='2'>Thu Dec 12 11:28:39 2019 EST</td></tr></table>"
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -355,7 +377,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.4"
+   "version": "3.7.5"
   },
   "varInspector": {
    "cols": {
diff --git a/qiskit/advanced/aqua/artificial_intelligence/qsvm_classification.ipynb b/qiskit/advanced/aqua/artificial_intelligence/qsvm_classification.ipynb
index 7e8c48a19..20a283d1a 100644
--- a/qiskit/advanced/aqua/artificial_intelligence/qsvm_classification.ipynb
+++ b/qiskit/advanced/aqua/artificial_intelligence/qsvm_classification.ipynb
@@ -1,25 +1,22 @@
 {
  "cells": [
   {
+   "attachments": {
+    "qiskit_header.png": {
+     "image/png": 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"
+    }
+   },
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<img src=\"../../../../images/qiskit_header.png\" alt=\"Note: In order for images to show up in this jupyter notebook you need to select File => Trusted Notebook\" align=\"middle\">"
+    "![qiskit_header.png](attachment:qiskit_header.png)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# _*Qiskit Aqua: Experiment with classification problem with quantum-enhanced support vector machines*_\n",
-    "\n",
-    "The latest version of this notebook is available on https://github.com/qiskit/qiskit-tutorial.\n",
-    "\n",
-    "***\n",
-    "### Contributors\n",
-    "Vojtech Havlicek<sup>[1]</sup>, Kristan Temme<sup>[1]</sup>, Antonio Córcoles<sup>[1]</sup>, Peng Liu<sup>[1]</sup>, Richard Chen<sup>[1]</sup>, Marco Pistoia<sup>[1]</sup> and Jay Gambetta<sup>[1]</sup>\n",
-    "### Affiliation\n",
-    "- <sup>[1]</sup>IBMQ"
+    "# Qiskit Aqua: Experiment with classification problem with quantum-enhanced support vector machines"
    ]
   },
   {
@@ -40,13 +37,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
+   "execution_count": 3,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T15:49:29.493476Z",
+     "start_time": "2019-12-12T15:49:25.657600Z"
+    }
+   },
    "outputs": [],
    "source": [
     "from datasets import *\n",
     "\n",
-    "from qiskit import BasicAer\n",
+    "from qiskit import Aer\n",
     "from qiskit.aqua.utils import split_dataset_to_data_and_labels, map_label_to_class_name\n",
     "from qiskit.aqua.input import ClassificationInput\n",
     "from qiskit.aqua import run_algorithm, QuantumInstance\n",
@@ -71,8 +73,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
+   "execution_count": 4,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T15:49:29.521460Z",
+     "start_time": "2019-12-12T15:49:29.511839Z"
+    }
+   },
    "outputs": [],
    "source": [
     "# from qiskit import IBMQ\n",
@@ -90,27 +97,36 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
+   "execution_count": 5,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T15:49:39.455533Z",
+     "start_time": "2019-12-12T15:49:29.543659Z"
+    }
+   },
    "outputs": [
     {
      "data": {
-      "image/png": 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PqTZVqL7TPLyJZSynDN7E137L8d154xatmakPLjLPgmTwpWuvtNVjd3pbXperceyHMnJ800WbOt91hpS5q7pm8lwyd90xVAYXmXODeNgk9tU059Ftl9iGMP03R7m8ORTwdwyVwUUKFiSDj4suDrnvBrTL5DmXVprGV1E/6SdU5h5TBhcpmE5wkYIFHWSbx1cBx+K75lvd8ocop8lK03Rpmk92pw4dX3NajzK4SMGCZPAX1jdxx5HHpg7gDG0wCih7sE3imfb9mZW5fQ1+urx88CjJcySf9rJGEYmmcaoqyZsAbAL4gpntd1ooecFC665GQ8p0Q+nX+XpLzJiOUT8hxki8FXwws8cB/MpLVCISlbc+OMllAMvT/q2ubvmQ+qyhR659GVLFWpnOV0ESbye4ma0AWAEubqKLSBrRHzaZlymSk1fbIbRO6gyhlZVj39tFUyx9W4q6Dy5SMJdR9C8DeCeARQD/BeAeMzvS8DszFzorc+d0VW3D5Qqb+j3a0/rifVpQOR6roWbwWXyVbGpsopvZbW0CE5F8qIkuUrAgg2xXL+5G2zeb5GBWs6iuSZdz9ZdQchp0K71p3vdWpzK4SMGSv5ssp6uqj2yQwyOyqd7QklJ1m3OIqY02mfvoR96hd5OJSMKqqkO7wroqdbvq5NAnD1UHP/aba3zVsh9TBhcpWPQMXkqGyyFrzeJ7ZN+1KMHQpuemvPPhmrn7TulWBhcpWJBRdJeCDzlf4VO9m8x3X7BPhmpbBLBq6Mc3RPwuJZtc9+nSwc9j9dTPNYouMs90gosULMog27SKLjkPUoXWpemc4/6al2f7+3I53qGeslQGFylY8oouOWamnB8gyXF/TZNjnLGPZ5fM7Xt/KYOLFCzZVNUhZPJ51OXxxJz74jEfJ+3TQmiz7mOHH8P6+obTZ5XBRQqWLIOPlZLJfcfdNAFiaNNCS+VSO22sy4QvVVUVkZmClmxq05+blcmBdBkqp9H0nPu5vvhsBYXqe7sst8300y7Lb0MZXKRgQfvgXd6R5fLZuiuuz9I9oTK375ZBNfPl0OJow2e8obZ91nJ9vf8tVNzK4CIF0wkuUjCnJjrJAwDuBbANwGEz+8egUVX0uZXWZeBmaE3csaHG7UOoZ/j7NM1dBtdCH7PGDE5yG4D7APw1gLcAuI3kW4JGJSJeuGTwGwD8zMyeBwCSXwFwK4CTriuZvJL5fCl9m6vfUCfQzJMhtLJ8fn9jcOmDvwnAixM/nxn9nYhkzuX1wR8AcMDMPjr6+XYAf2FmH6t8bhnA8ujH/QCe9h9ua4sA1lMHMZJLLLnEASiWaVzjuNrM9jR9yKWJfhbAVRM/7x393QXMbAXACgCQXDWzJYdlB5VLHEA+seQSB6BYYsTh0kT/AYA/I3kNydcB+CCAf/UVgIiE05jBzexVkh8D8O/Yuk121MyeCR6ZiPTmdB/czL4B4BstlrvSLRzvcokDyCeWXOIAFMs0XuMI8uIDEcmDpqqKFMzrCU7yAMmfkvwZyU/5XHbLOI6SPEcy6a06kleRfJTkSZLPkLw7YSw7SX6f5I9GsRxKFcsonm0kf0jy64njOE3yJySfIrmaOJbLST5I8lmSp0j+Ze9l+mqij6a0Pgfg3diaDPMDALeZmfOMN19I3gRgE8AXzGx/7PVPxHEFgCvM7EmSCwDWAPxNon1CALvMbJPkDgBPALjbzL4XO5ZRPJ8AsATgDWZ2S4oYRnGcBrBkZsnvgZO8H8B/mNnh0R2r15vZS32W6TOD/35Kq5m9DGA8pTU6M3scwK9SrLsSxy/M7MnRnzcAnEKiWYC2ZXP0447Rf0kGYEjuBfA+AIdTrD9HJC8DcBOAIwBgZi/3PbkBvye4prTWILkPwHUATiSMYRvJpwCcA/CImaWK5XMAPgngd4nWP8kAfIvk2mg2ZirXADgP4Nio63KY5K6+C9UgWwQkdwN4CMDHzezXqeIws9fM7K3Ymo14A8no3ReStwA4Z2Zrsdc9w9vN7HpsPS35t6PuXQrbAVwP4J/N7DoA/wOg9ziWzxPcaUrrvBn1dx8C8ICZfS11PAAwavo9CuBAgtXfCOD9o77vVwDcTPKLCeIAAJjZ2dH/zwF4GFtdzRTOADgz0ap6EFsnfC8+T3BNaa0YDWwdAXDKzD6bOJY9JC8f/flSbA2GPhs7DjP7tJntNbN92PqOfNvMPhQ7DgAguWs0+IlRc/g9SPSQlJn9EsCLJN88+qt3ocUj2bN4K7qY05RWkl8G8E4AiyTPALjHzI4kCOVGALcD+Mmo7wsAfz+aGRjbFQDuH93tuATAV80s6S2qDPwhgIe3rsPYDuBLZvbNhPHcBeCBUYJ8HsDBvgvUTDaRgmmQTaRgOsFFCqYTXKRgOsFFCqYTXKRgOsFFCqYTXKRgOsFFCvb/Ykxs+mHyu8oAAAAASUVORK5CYII=\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x12b20fa20>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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qclA3yLZd+4+F9KksHxxoextrDp+nqD8HdYNkGSkP9Pexad2qAqqxKvB5imbwOuoGOdlIuU9CwPDgAFdecgHrR4YZm5hkzdbdnHf5zazZupuxiclii7VCLPQuq3WewurDI+oG2bRu1bzREyQj6FY4t3iU1TtO9i7L5ynqxSPqBlk/MsyVl1zA8ODAo0bQc3mUVYKSrkJ+sndZPk9RLx5RN8z6keG2o2KPsgpWYo/kk73L8nmKevGIugd5lFWwEnskZ32XZdXmEXUP8iirYCX3SM7yLsuqzUHdg1ovWq+tLciyFcl0x0LHzTJwUPcoj7IK5KuQ2xJ5jtqs2xraI9mKU4sRtbfAWu01sEeyFafyQe3NGWbW69pOfUg6R9LXJN0t6QeSLiuisBZvzjCzXpdlRP0I8I6IuEPSE4A9kr4SEXd1uTbAmzPMzNqOqCPipxFxR/r1Q8DdQGFzDt6cYWa9rqNVH5JWAiPA7Qv8bKOkcUnjU1NT+VRHsjljoL9v3jFvzjCzXpI5qCWdCXwOeHtEPHjizyNie0SMRsTo0NBQbgV6C6yZ9bpMqz4k9ZOE9HURsbO7JT2aN2eYWS/LsupDwDXA3RHxge6XZGZWQSW1qoVsUx9rgDcAL5F0Z/rxR12uy8ysOlqtao8cAOJ4q9qCwrrt1EdE3AaogFrMzKrpVK1qC9hx6l4fZmbtlNyq1kFtZtbOyVrSFtSq1kFtZtbO2i1Ja9q5CmxV66A2M2un5Fa1le+eZ2ZWCSW2qnVQm1nu3EM+Xw5qM8uVe8jnz3PUZpYr95DPn4PazHLlHvL5q0dQl7jH3qwUNf6bdw/5/FU/qEveY29WuJr/zbuHfP6qH9Sn2mNv1kQ1/5t3D/n8VX/VR8l77M0K14C/efeQz1f1g3rZivQt4ALHzZrIf/O10+1149Wf+ih5j71Z4fw3XyutdeOT0zMEx9eNj01M5vYY1Q/qkvfYW7ONTUyyZutuzrv8ZtZs3Z3ri2vR/DdfK0WsG6/+1AeUusfemqvSO+j8N18bRawbr/6I2qxLvIOuIDVeE55FEevGHdTWs7yDrgA1XxOeRRHrxh3U1rO8g64ANV8TnkUR68brMUdt1gWb1q2aN0cN3kGXuwasCc+i2+vGHdTWs1ovLPdN7iKvCc+Fg9p6mnfQddnaLcmc9NzpD68J71jbOWpJH5V0WNL3iyjIzBpk9QZ47utA6ck29SXfe+lhR7KcTPwY8PIu12FmTbR3B3zveoj0PEDMJt83aNVHEdoGdUR8A/hFAbWYWdP0wKqPInh5npl1T4+s+ui23IJa0kZJ45LGp6am8rpbM6uzk63u8KqPjuQW1BGxPSJGI2J0aGgor7s1szpzJ8BceOrDzLrHnQBz0XYdtaRPAy8GzpJ0EHh3RFzT7cLMrCHcCXDJ2gZ1RLy2iELMzGxhnvowM6s4B7WZWcU5qM3MKs5BbWZWcQ5qM7OKc1CbmVWc+1GbmS3R2MRkVy9A4aA2M1uCsYnJeZd0m5yeYfPOfQC5hbWnPszMlmDbrv3zrrsJMHN0lm279uf2GA5qM7MlODQ909HxxXBQm5ktwfLBgY6OL4aD2sxsCTatW8VAf9+8YwP9fWxatyq3x/DJRDOzJWidMPSqDzOzCls/MpxrMJ/IUx9mZhXnoDYzqzgHtZlZxTmozcwqzkHdsncHfPB8uGIw+bx3R9kVmZkBXvWR2LsDbnobHE13Eh05kHwPviinmZXOQQ3w1fceD+mWozPJcQe19Yhud4CzxXNQAxw52Nlxs4YpogOcLZ7nqAGWrejsuFnDFNEBzhbPQQ2wdgv0n9BApX8gOW7WA4roAGeLlymoJb1c0n5JP5J0ebeLKtzqDXDxVbDsHEDJ54uv8vy09YwiOsDZ4rWdo5bUB3wY+EPgIPBdSZ+PiLu6XVyhVm9wMFvP2rRu1bw5asi/A5wtXpaTiRcCP4qIewEkfQZ4NdCsoDbrYUV0gLPFyxLUw8CBOd8fBH73xBtJ2ghsBDj33HNzKc7MitPtDnC2eFnmqLXAsXjUgYjtETEaEaNDQ0NLr8zMzIBsQX0QOGfO9yuAQ90px8zMTpQlqL8LPFPSeZIeA/wZ8PnulmVmZi1t56gj4hFJbwF2AX3ARyPiB12vzMzMgIxbyCPii8AXu1yLmZktwDsTzcwqThGPWsCx9DuVpoCftLnZWcDPcn/w4jXleUBznoufR7U05XlAd5/L0yNiwSVzXQnqLCSNR8RoKQ+eo6Y8D2jOc/HzqJamPA8o77l46sPMrOIc1GZmFVdmUG8v8bHz1JTnAc15Ln4e1dKU5wElPZfS5qjNzCwbT32YmVWcg9rMrOIKD+qmXC1G0kclHZb0/bJrWQpJ50j6mqS7Jf1A0mVl17QYkh4n6TuSvpc+j/eUXdNSSOqTNCHpC2XXshSS7pO0T9KdksbLrmexJA1KukHSPelr5QWFPn6Rc9Tp1WL+kzlXiwFeW8erxUh6EfAw8ImIOL/sehZL0tnA2RFxh6QnAHuA9XX7fyJJwBkR8bCkfuA24LKI+HbJpS2KpL8BRoEnRsRFZdezWJLuA0YjotYbXiR9HPhmRFydNqd7fERMF/X4RY+oj10tJiJ+DbSuFlM7EfEN4Bdl17FUEfHTiLgj/foh4G6Si0XUSiQeTr/tTz9qeaZc0grglcDVZddiIOmJwIuAawAi4tdFhjQUH9QLXS2mdqHQVJJWAiPA7eVWsjjpdMGdwGHgKxFRy+cBfAh4J/CbsgvJQQBflrQnvQpUHT0DmAKuTaejrpZ0RpEFFB3Uma4WY8WTdCbwOeDtEfFg2fUsRkTMRsTzSC5ucaGk2k1JSboIOBwRe8quJSdrIuL5wCuAv06nDOvmdOD5wEciYgT4JVDo+bWig9pXi6mgdE73c8B1EbGz7HqWKn1beivw8pJLWYw1wKvSud3PAC+R9KlyS1q8iDiUfj4M3Egy/Vk3B4GDc96h3UAS3IUpOqh9tZiKSU/CXQPcHREfKLuexZI0JGkw/XoAeClwT7lVdS4iNkfEiohYSfL62B0Rf15yWYsi6Yz0BDXpVMHLgNqtkoqI+4EDklalh9YChZ5sz3ThgLw06Woxkj4NvBg4S9JB4N0RcU25VS3KGuANwL50fhfgXenFIurkbODj6cqi04AdEVHrpW0N8DTgxmQswOnA9RFxS7klLdpbgevSAea9wJuLfHBvITczqzjvTDQzqzgHtZlZxTmozcwqzkFtZlZxDmozs4pzUJuZVZyD2sys4v4flplGuyNelOkAAAAASUVORK5CYII=\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1154d1320>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     },
     {
@@ -152,8 +168,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
+   "execution_count": 6,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T15:51:08.407289Z",
+     "start_time": "2019-12-12T15:51:08.213734Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -172,7 +193,7 @@
     "feature_map = SecondOrderExpansion(feature_dimension=feature_dim, depth=2, entanglement='linear')\n",
     "qsvm = QSVM(feature_map, training_input, test_input, datapoints[0])\n",
     "\n",
-    "backend = BasicAer.get_backend('qasm_simulator')\n",
+    "backend = Aer.get_backend('qasm_simulator')\n",
     "quantum_instance = QuantumInstance(backend, shots=1024, seed_simulator=seed, seed_transpiler=seed)\n",
     "\n",
     "result = qsvm.run(quantum_instance)\n",
@@ -183,7 +204,7 @@
     "    'algorithm': {\n",
     "        'name': 'QSVM'\n",
     "    },\n",
-    "    'backend': {'provider': 'qiskit.BasicAer', 'name': 'qasm_simulator', 'shots': 1024},\n",
+    "    'backend': {'provider': 'qiskit.Aer', 'name': 'qasm_simulator', 'shots': 1024},\n",
     "    'feature_map': {'name': 'SecondOrderExpansion', 'depth': 2, 'entanglement': 'linear'}\n",
     "}\n",
     "algo_input = ClassificationInput(training_input, test_input, datapoints[0])\n",
@@ -198,8 +219,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
+   "execution_count": 7,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T15:50:38.389886Z",
+     "start_time": "2019-12-12T15:49:29.079Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -210,12 +236,14 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x12ed95978>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -236,17 +264,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
+   "execution_count": 8,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T15:50:38.395382Z",
+     "start_time": "2019-12-12T15:49:29.986Z"
+    }
+   },
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x130d01b70>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -261,14 +296,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
+   "execution_count": 9,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T15:50:38.400460Z",
+     "start_time": "2019-12-12T15:49:30.364Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "testing success ratio:  0.8\n"
+      "testing success ratio:  0.85\n"
      ]
     }
    ],
@@ -278,7 +318,7 @@
     "feature_map = SecondOrderExpansion(feature_dimension=feature_dim, depth=2, entanglement='linear')\n",
     "qsvm = QSVM(feature_map, training_input, test_input)\n",
     "\n",
-    "backend = BasicAer.get_backend('qasm_simulator')\n",
+    "backend = Aer.get_backend('qasm_simulator')\n",
     "quantum_instance = QuantumInstance(backend, shots=1024, seed_simulator=seed, seed_transpiler=seed)\n",
     "\n",
     "result = qsvm.run(quantum_instance)\n",
@@ -292,8 +332,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
+   "execution_count": 10,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T15:50:38.406132Z",
+     "start_time": "2019-12-12T15:49:30.848Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -304,12 +349,14 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x132c716a0>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -322,19 +369,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 11,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2019-08-22T01:49:36.816822Z",
-     "start_time": "2019-08-22T01:49:36.806994Z"
+     "end_time": "2019-12-12T15:50:38.410937Z",
+     "start_time": "2019-12-12T15:49:31.209Z"
     }
    },
    "outputs": [
     {
      "data": {
       "text/html": [
-       "<h3>Version Information</h3><table><tr><th>Qiskit Software</th><th>Version</th></tr><tr><td>Qiskit</td><td>None</td></tr><tr><td>Terra</td><td>0.9.0</td></tr><tr><td>Aer</td><td>0.3.0</td></tr><tr><td>Ignis</td><td>0.2.0</td></tr><tr><td>Aqua</td><td>0.5.6</td></tr><tr><td>IBM Q Provider</td><td>0.3.2rc1</td></tr><tr><th>System information</th></tr><tr><td>Python</td><td>3.7.4 (default, Aug 13 2019, 15:17:50) \n",
-       "[Clang 4.0.1 (tags/RELEASE_401/final)]</td></tr><tr><td>OS</td><td>Darwin</td></tr><tr><td>CPUs</td><td>4</td></tr><tr><td>Memory (Gb)</td><td>16.0</td></tr><tr><td colspan='2'>Wed Aug 21 21:49:36 2019 EDT</td></tr></table>"
+       "<h3>Version Information</h3><table><tr><th>Qiskit Software</th><th>Version</th></tr><tr><td>Qiskit</td><td>0.14.0</td></tr><tr><td>Terra</td><td>0.11.0</td></tr><tr><td>Aer</td><td>0.3.4</td></tr><tr><td>Ignis</td><td>0.2.0</td></tr><tr><td>Aqua</td><td>0.6.1</td></tr><tr><td>IBM Q Provider</td><td>0.4.4</td></tr><tr><th>System information</th></tr><tr><td>Python</td><td>3.7.4 (default, Aug  9 2019, 18:34:13) [MSC v.1915 64 bit (AMD64)]</td></tr><tr><td>OS</td><td>Windows</td></tr><tr><td>CPUs</td><td>2</td></tr><tr><td>Memory (Gb)</td><td>7.9987335205078125</td></tr><tr><td colspan='2'>Thu Dec 12 13:33:45 2019 Eastern Standard Time</td></tr></table>"
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
diff --git a/qiskit/advanced/aqua/chemistry/declarative_approach.ipynb b/qiskit/advanced/aqua/chemistry/declarative_approach.ipynb
index 3dfba0036..f4d74c628 100644
--- a/qiskit/advanced/aqua/chemistry/declarative_approach.ipynb
+++ b/qiskit/advanced/aqua/chemistry/declarative_approach.ipynb
@@ -1,25 +1,22 @@
 {
  "cells": [
   {
+   "attachments": {
+    "qiskit_header.png": {
+     "image/png": 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"
+    }
+   },
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<img src=\"../../../../images/qiskit_header.png\" alt=\"Note: In order for images to show up in this jupyter notebook you need to select File => Trusted Notebook\" align=\"middle\">"
+    "![qiskit_header.png](attachment:qiskit_header.png)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# _*Qiskit Chemistry, Declarative Approach*_ \n",
-    "\n",
-    "The latest version of this notebook is available at https://github.com/Qiskit/qiskit-iqx-tutorials.\n",
-    "\n",
-    "***\n",
-    "### Contributors\n",
-    "Richard Chen<sup>[1]</sup>, Antonio Mezzacapo<sup>[1]</sup>, Marco Pistoia<sup>[1]</sup>, Stephen Wood<sup>[1]</sup>\n",
-    "### Affiliation\n",
-    "- <sup>[1]</sup>IBMQ"
+    "# Qiskit Chemistry, Declarative Approach"
    ]
   },
   {
@@ -40,7 +37,12 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:20:20.867722Z",
+     "start_time": "2019-12-12T17:20:14.502252Z"
+    }
+   },
    "outputs": [],
    "source": [
     "from qiskit.chemistry import QiskitChemistry\n",
@@ -69,7 +71,12 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:20:20.890416Z",
+     "start_time": "2019-12-12T17:20:20.887885Z"
+    }
+   },
    "outputs": [],
    "source": [
     "# from qiskit import IBMQ\n",
@@ -80,7 +87,12 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:20:20.913096Z",
+     "start_time": "2019-12-12T17:20:20.908859Z"
+    }
+   },
    "outputs": [],
    "source": [
     "from qiskit import Aer\n",
@@ -91,6 +103,10 @@
    "cell_type": "code",
    "execution_count": 4,
    "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:20:20.937840Z",
+     "start_time": "2019-12-12T17:20:20.933397Z"
+    },
     "scrolled": true
    },
    "outputs": [],
@@ -118,8 +134,22 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:20:32.858890Z",
+     "start_time": "2019-12-12T17:20:20.956876Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/paul/opt/anaconda3/envs/qiskit/lib/python3.7/site-packages/qiskit/providers/models/backendconfiguration.py:337: UserWarning: `dt` and `dtm` now have units of seconds(s) rather than nanoseconds(ns).\n",
+      "  warnings.warn('`dt` and `dtm` now have units of seconds(s) rather '\n"
+     ]
+    }
+   ],
    "source": [
     "solver = QiskitChemistry()\n",
     "result = solver.run(qiskit_chemistry_dict, backend=backend)"
@@ -135,13 +165,18 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:20:32.880407Z",
+     "start_time": "2019-12-12T17:20:32.876893Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Ground state energy: -1.1361894423809882\n"
+      "Ground state energy: -1.1361894404150168\n"
      ]
     }
    ],
@@ -159,7 +194,12 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:20:32.904052Z",
+     "start_time": "2019-12-12T17:20:32.897207Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -167,23 +207,23 @@
      "text": [
       "=== GROUND STATE ENERGY ===\n",
       " \n",
-      "* Electronic ground state energy (Hartree): -1.892156886552\n",
-      "  - computed part:      -1.892156886552\n",
+      "* Electronic ground state energy (Hartree): -1.892156884586\n",
+      "  - computed part:      -1.892156884586\n",
       "  - frozen energy part: 0.0\n",
       "  - particle hole part: 0.0\n",
       "~ Nuclear repulsion energy (Hartree): 0.755967444171\n",
-      "> Total ground state energy (Hartree): -1.136189442381\n",
+      "> Total ground state energy (Hartree): -1.136189440415\n",
       "  Measured:: Num particles: 2.000, S: 0.000, M: 0.00000\n",
       " \n",
       "=== DIPOLE MOMENT ===\n",
       " \n",
-      "* Electronic dipole moment (a.u.): [0.0  0.0  -0.00006376]\n",
-      "  - computed part:      [0.0  0.0  -0.00006376]\n",
+      "* Electronic dipole moment (a.u.): [0.0  0.0  -0.00015451]\n",
+      "  - computed part:      [0.0  0.0  -0.00015451]\n",
       "  - frozen energy part: [0.0  0.0  0.0]\n",
       "  - particle hole part: [0.0  0.0  0.0]\n",
       "~ Nuclear dipole moment (a.u.): [0.0  0.0  0.0]\n",
-      "> Dipole moment (a.u.): [0.0  0.0  0.00006376]  Total: 0.00006376\n",
-      "               (debye): [0.0  0.0  0.00016206]  Total: 0.00016206\n"
+      "> Dipole moment (a.u.): [0.0  0.0  0.00015451]  Total: 0.00015451\n",
+      "               (debye): [0.0  0.0  0.00039272]  Total: 0.00039272\n"
      ]
     }
    ],
@@ -201,19 +241,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 8,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2019-08-22T01:50:03.559587Z",
-     "start_time": "2019-08-22T01:50:03.549101Z"
+     "end_time": "2019-12-12T17:20:33.025024Z",
+     "start_time": "2019-12-12T17:20:32.929809Z"
     }
    },
    "outputs": [
     {
      "data": {
       "text/html": [
-       "<h3>Version Information</h3><table><tr><th>Qiskit Software</th><th>Version</th></tr><tr><td>Qiskit</td><td>None</td></tr><tr><td>Terra</td><td>0.9.0</td></tr><tr><td>Aer</td><td>0.3.0</td></tr><tr><td>Ignis</td><td>0.2.0</td></tr><tr><td>Aqua</td><td>0.5.6</td></tr><tr><td>IBM Q Provider</td><td>0.3.2rc1</td></tr><tr><th>System information</th></tr><tr><td>Python</td><td>3.7.4 (default, Aug 13 2019, 15:17:50) \n",
-       "[Clang 4.0.1 (tags/RELEASE_401/final)]</td></tr><tr><td>OS</td><td>Darwin</td></tr><tr><td>CPUs</td><td>4</td></tr><tr><td>Memory (Gb)</td><td>16.0</td></tr><tr><td colspan='2'>Wed Aug 21 21:50:03 2019 EDT</td></tr></table>"
+       "<h3>Version Information</h3><table><tr><th>Qiskit Software</th><th>Version</th></tr><tr><td>Qiskit</td><td>0.14.0</td></tr><tr><td>Terra</td><td>0.11.0</td></tr><tr><td>Aer</td><td>0.3.4</td></tr><tr><td>Ignis</td><td>0.2.0</td></tr><tr><td>Aqua</td><td>0.6.1</td></tr><tr><td>IBM Q Provider</td><td>0.4.4</td></tr><tr><th>System information</th></tr><tr><td>Python</td><td>3.7.5 (default, Oct 25 2019, 10:52:18) \n",
+       "[Clang 4.0.1 (tags/RELEASE_401/final)]</td></tr><tr><td>OS</td><td>Darwin</td></tr><tr><td>CPUs</td><td>4</td></tr><tr><td>Memory (Gb)</td><td>16.0</td></tr><tr><td colspan='2'>Thu Dec 12 12:20:33 2019 EST</td></tr></table>"
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -265,7 +305,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.4"
+   "version": "3.7.5"
   },
   "varInspector": {
    "cols": {
diff --git a/qiskit/advanced/aqua/chemistry/dissociation_profile_of_molecule.ipynb b/qiskit/advanced/aqua/chemistry/dissociation_profile_of_molecule.ipynb
index a08ce46e2..d90a83fd3 100644
--- a/qiskit/advanced/aqua/chemistry/dissociation_profile_of_molecule.ipynb
+++ b/qiskit/advanced/aqua/chemistry/dissociation_profile_of_molecule.ipynb
@@ -1,25 +1,22 @@
 {
  "cells": [
   {
+   "attachments": {
+    "qiskit_header.png": {
+     "image/png": 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K7KdEZsu7QvYk/wuyZbahTeNE7Q/YTF8+gXY5+vSriPjkPZAYM/2yHpv9pM2pQ5svlCDWSeTlgxgzkXdQVLYP0OGiDNpS7mHR5m44iMn2bRZ5x+Xg0dmSbMPuI8d60RtCf8Fm1tofM8FFvh+YEM1nYkBKspxfaq3Y7HgsML9bkPMsFsT6C3kjQIzxyDsnJttkdKgOg3ZNNCHidR2IyXasGXnlavDobEm28iIWOYYSW8Fl2MJcvW8QBS5JOIuIL1aBdNR5ZTVmbLE/hgKYqUcO+54CRLqPvFQQIxt518Vk+zY6/AAdyLfgZT8Qk+3gJuTd8gPPzpZkqz5iL+JBOHD8t5kc4Vb9OFYDLvCe04RovZwEEgmd83utI1r9Fh1wAm6yyDmrBZFeSZLtYzHZLkGHUuhI4RkfEJPtgBrkPQoFz86WZEsl1iLH+ElLg4X8/clUe2nJABq6FPcbItbukIMEvIfuuNNkwVa3Z7SEusVoTySTAnEskmRbLyJbYXjDpTrdIuIp8qriwMOzJdkqStGmfjg0U/T72oCtrPrKX4tju0qFKahGxOvDoLs0mesv1hpZbFX3aT8FNMuoRpuNDIjijZJki7SIbA+gQ6RE2QZUIK8+FTw9W5Ktz2l7n5fU/PdMuog8s/7Jb+8mqaEzfb9GxIbO1nm97KAzutFbymsNVnQwnRingVbMSQtyrvqDKOESZesnIttf0WG0NNmqypFnGA6eni3JlkqpRY5hLfDogOK/UcBseHVh5+S+jHOIArLRTxGxYmLn1ToDIW3avLKKemGzaD47zY8G3vsGe8pDaRAjQaJso0Vk+xk6LJEm2/3Is0wCj8+WZKtYhDav0kBIETT7vAUFrMbGJye3FQzwea3BPnZeoTtt6Zf5dZVt89V2wH3LFi8dkvXe11erDSYWBeoP5vvLQah/JdqsYcS1J1G2qSKyXYMOP0qS7VIUmAMkW5Kt9xF7lReU4EyunfBzDTqxGJpe/v398vGxPv/5Tx8bPkQq6w4iPpwF7XOq3BH7fxQhg2fvPH6/1mC2opNHe4ZpZeCMPmpBznkNiDFcomwzRGQ7Hx0aEyXIdpwVne7fJFuSbUwlcgzroQ3aJ3X1X0Z0xpr1jVVXftsya2hfb8rLzl5iwCoroulYaFfZetlRqpCMgmXfnv2n1mC0sOis+khxtIoGAeEouS6FAhFSJcq2n4hshyDvH/9uZxtf99peSZItyTbfXk39MGgP4z9qx3UTvo61GAx1z68f+WL1rJyUcB/aNtZNvICIzxZBJ7jLvEMTRsxY9slvfz2u1evNFmyj+vfSFI0C2pNcgzbz5CBCjETZhorIFp4h71xMN7PVViDvayDZkmxb/5ewN7XQLkrGhI/bfKkO28GaTQZ9Y82jmxd//XLr0tKfWETLpTRfH0ZBQTOaYbx9A0P7xqWOnFT0wYZPDpy+8ehVg95gMrPYnsc/l2QEKGQUtEt9yYqc75UgQqBE2fqIyXYPClj2xXcnW+oo8o7SJFsOyZa+jBzTPmiLVqo1Go2PWqkKHvzuj3easAOs1WwyGgwm5BgfXf/70oVTx+1OnLl06a8bFQ+f19Q3NDUZjEazhcWOvDq3dXKcVqlS+2i0GrVS5gVt7DIi504QiKCQJlsriMk2A51Yz64fpXE32y0ocEVGsuWQbCNqkKOfC69hvAN0QUGBfhqtj1qtZOTqiGEL912vwy6xVqvV0srKYbEr5qendxQm+zMKRqX29tFo/AKCgoICNAwFziY3IccwmAIRDJJkWycqW/gOX2e9+tWHeTG06Gyno5PlJFsOyXacvaraJBDwUgWERUZGhAUHBQZw3Wq8vVUMw8hpRpcybdPhe3UoHePzP75amBOjkdMKhlHaotVqfAMCg4LDIiL7hgeqKBCIeoY24o6UqpQk2yfisg2qwXYZrn6xIIMRkW2qHp0Y+pFsSbYAq5pvBVq+WSYwOr5fbEwU160uKMBfq9VqfLzVKiVD0wAyRh2YMmn5vvNPGljsFv2La79tmzs8WssoZF5AyRS28bGtWj//wCBdWEREVGxs//gYnQocmAtW5HzCgAh3kZcNYoxC3i1x2cI0FjtkOv1usIvZ6h7ja/6gSLYkW/gNOeYfoQWliUpKSU4cOCA2NioiPITrNsDfV+utUshoaN3ZRMkYpXd4+qSSHT9fvFtjFJmvqfaf8oMfL56aHe2vZmQ0/wYikClUaq3WPyAgSBcabq92YEJyalIsv1fqEyNyLmpBhD+RNxHEmI68SyKzhXksdoK9UEi7kG15ObbxAcmWZKu6KzzTXB6WnDlkUHpaStJA7n7bNzwiXBekVStkjmIFKJmcUWlC4gZNmLNs+76jf9x6WCncUCxkNdS9eHznr9/3f7Rm3qTMfmFaNWMLludol5LJ1dogXTh3r42J7TcwISUtfVBmZkq4HOzm6pHzrC+IcBR5c0CMEuT9JDZbeNeKnaqYTnWRbfuaokm2Hp9tTANymsbZo43MzMkZMSxrSHpacuKAeO5+GxqgllPgXGx7+dpmpb5hcYkZ2bmTp89774OV6+3WLil5r3hG/ujsQUlxEf4+ahXDyPlc22cbL6v9Q6Ni4/rbqx2SNXRETm5mtAI4Q2qRY8miwXUbkbfY7cPjVorOFrLvY+fKw93JFk+TbD0+29EscirjAGR9c96aOG5U7sgRQ7luU1MGRgWqbLdZEIGiKa8+IJMpmBYKhVwmk9EUBWJ4Aa0K7DswOTU9Y0j2iJG5o8ZNnJwTLQMIe8gip0gOrstH3mYQYw/y8sRnC6rdJuxU1UgXs31exCJvHsnW07MtQQ573RvCxr1dWJD/1oRxuVy4wwYPCPGhwQvE8qLkcpWPigK38eV6B/fPGMZFmzs2b1J+QeHMcZFAN69JbWDAdVHI+wrE2I+8cBHZ8kI312BnLONdyrZ6IOxFXl04ydbDs92DHPMvqhHvvlNcNLNwSv7EvLwxWXEad8OjlT6+umA1SMM7asjovLwJ+QUzZs4ufufdXNWXJuTsV4EIgnJugBj/CMIBUdny1IV772DH6vu7kG1DBoDvC+QdIdl6eLZH7dmWTV9aurBkfnHR2zOmT84MlYP7VIFhUf3DFCARefCgidNnvF1UvKBkYenSwr1G5JR7gwin0IENcPMd7E+Jz5YXMHFbuQnbd1/ZZbaGkcCZhgKFJFuPzlZxFTmW31esXLZk8fvvLJg/MzMIukUWHJ80ZFAISMcvY/q8+e8uLF2ybOWKg2bkPAoGEbYi7y1wXSHydriXLU+VPX/3iccstlHaVbbmCW2fT1KtI9l6crb+j5Fj/nX9hrWrlixdWpIbBN1E+aUNHf3WEA1IyDe7ZOnS5avXbVi/39wy1xNhKvJ2g+v2Iu9t97MV8h5Ssq8anVR5d54tW9je08B+Itl6crYRjc2D5O3bNq/fuG5qGHSfKnnitOLiZBqkFFKwZtOmLdt3fG9EjjGTcnNN6rp7ByyzkdJkayMbdRaFFnSebQm0WoIC+SRbD842iUWOoezT3bs+WplGgQTo8JnvLds0LwKklbzs448++eyHRrQZR7v7XIIMt47FuAASZMvLb0TewU6zXQEO8uvIq/Qj2Xputjlo0/jL119/+24gSEPz9sadX32TrwBp+RV/8+2+n2rRplDu7m6nMrd2V82XNlsYjrx6eSfZbgOBwVbk7SPZem62+WhTf/jnsqlykAg9as+Bo5e/HQQSoyft/+VwDdosVIAIASZ0sEaDawaw6GDylzhb+BZ5KR1newycfIwCY0m2HpvtbLSpO3V0KkgnduOl68+q1mpBahOPnmrOdjkDYvyKvI/ANV8h7yBInW1QnbA/V5+4p32OvCcakq2nZvse2jSULwAJyd6+2mDGCzkgudmXatFmKyMud+Tp48AViUbkTZE8WziBDjNdzhbyUeBzkq2nZrscbZq+VIKU+h2w9bHZD6Qm/6gRbbaJy1b+CnlXFNA15S3k1Sulz1bQZqnr2cIh5LHDSbYemu0ytKkZBpKiZt5HxL/zQHKpL9DmS6XoSHjboWufosA2kD7bYnSYLyLbiEbkPVCRbD04W+sVNUgr7HMzonlPMEiNOW9Fzrcis9XVIo8dJWpUjZUad7IdUa7renZiN0lEtrAIBXaSbD0z2+3IsRwGqb11DRFvTwHJlZndyRZKUKBuOHRuTCMKzAI3si0y4TVf6Ng+dBgsJlv6CvKsg0m2HpntOnu25yiQWOAOA6L16yiQ2jGLW9nSV1HAOAU6U2RGgXJwI9sNXb3Hw210iBKTLaRZkHebIdl6YrZL0eZFAkht1CVEfDSbBmnFPEObr5Ug0mAWBdj3oWMrUIjNEJ8tcwDtTjHQAa0VW9XIRGULu1BgA8nWc5ekTJ+B1DTrGhFxf3+Q1sdGtNnOgFhfopNjA6B9aWfQydcgOtvAS9jiRiK07wd02AfisvV5jDxzMsnWA7NdgnYNM0Bq2acR8XmJHKQ0vRHttonPNuAlOrHs1UFbkd+z6KTCT3S28Q/QwbiYgnaUIm+yyGxhAgpclbmT7TmSba/Odh42q8wDiSmX1iDi4XSQ0LhKbLaaAdHSG9BZw7cFGueyCw8Y0FllXxCbLV2BQmdioI08CzoYvMVmC7+gwAp3sv2CZNursy3EFlVTQWJphxCxZokSJFP0ClssUoB4Iwz4OtPJJTNyEoIUusTcwpUXrPi6hhQQnS0k1qAQe6pQCUK5Z1FgG4jONrQeecb+bmRbig5ly3lToFcg2eZhK/1aNUhK/n4lIp7MBolodxmx1Sw5uGGSBcUxjwI3soX0enRW9/nUrEg5AKjic+ddRqHnPuKzhRIUKKfEZzse23UMegWS7RB0sJzMAEkNKEPExnUakERuuRUd8mTgjtkozixwK1vIbsK22Mqbr7CNQnAjW6ocBRaJzzaWZNurs40zIa9mUyhIiJrzEBEv5YIE4j83IM+cTYNbSszoutrJ4Ga2kGtE15SBO9lCkhl5TTGis4U/Sba9OdvgKhRg7y0KAulEfW1FNGwPgO6KWP2CdQoqiQL3ZNxDV/3ZF9zOFka/RFcclLuXLWxFgTPis023kmx7cbbeFejEWvFhOEhm2h1EvDYRumfgtqdWdPI4HNyl3ouu2aWAbmQLuoPYtRMMuJmt6iEKzBedLXxOsu3F2cJ55FiuPcZW7LM9GTRII+QTM6L5szBwnzp33ysWW909bUbOVR9w38SX2LUXEwDcypY3ux47x+5WgrvZwlgUqA8Xna1/Jcm2F2f7A3JMXw44jjzjqbkRIIm8K/ZHy4GbqITlf5mQtz96txE5B1XQDbrttdi5+wuU0O1sIfJs579INoD72cJ+597EZgtBB0i2vTfbVcixnAPlgofIYyt/nBIM3ee/1YDI7osGN8jiSk7Uo8C1KTI4ZUHODga6RTX3GnasPJ8CkCBboAo7/mVerFdBt7LV1aLATJHZ2kx4RrLtrdlOsVf6UAcQuv6RkRWU+7xs3gAfRgbdMvwci/hkgRzEoBTK4BFrL9Sz6GA13FjkDxB4l0XOAgV0V3ZZI7bDenVPNvDczJaX83MDtuPMFAVA97KFeShQoxOfLWjyFn95sZpk2wuzTTMhp3aIrZb47Q8NVhR4eXptbpiKocFt3itqEPFgshfHxWQZbdrcb+6YUMBiuF4aApykantbOTR0H9VvxvYztcgznFk/SgNSU4zcfqYSeYY/dk2PBHcRJFt+aUI/G+xiV9/Sm1HI8vC3Zbl91YycgnZ5CUFbg46yiFWlSq/XQVsyRhmUPnfvX3UsCrCmpgslgWA3XY+cV3EgmfHo8Dn0GN+M0flFJXMmj0yNlgPRXSRb+hxyjLuhRdCsYzUGKzqxPP5964xUHdcuLYiV+6yPF6cPp+MUmUVViHgik7vOcWGfNvVSNKP07TduedmdJnRmMTzb/5YGWmw1IudvX5BMqjDb3oEg2cKn9jjOyPmQ0zdcaTSy+JrGO4e3zMyO0KpsN94+LcV6dd1t4k8sYu0aHz7bPvxroQ/QCqVPcMpby74rr2axTbOvTi/qJ4NW9DELcvYpSbaejWQ7C22eRICAduyntxqNVmzD8Kz8+7Uzh/ULUMplMrCV2GW2suLHiHguB/ibLfeK//TxomkZ4xc7ZMqyL049qLPi61iz4eXZ5RkMCAQ/spe9UEGy9Wwk20QDcprywFnA6J3ltQYzi+2wvrxz5uuVc8alxgR6q5QKhZymKQBhtEJx+6yI+i0BtqopSqZQMEq1Ljpl7OxVe4/feG7E9liM+keHPkhlwFluA3LMOTTJ1rORbL0fIMewCdpQp5X+9qDROV0h1vjy9sXDX20qnTVpZHq/0CA/jVqlUioZjqIVwzDKwgpEvDItKCw2dVhe0aJNXx2+eLtKz2IHrEb9y4t7Cvq20+YqA3IehYNnZ0uQbGF/84YLBbSDCh21+sj9JoPZip0yNdVUPrh26fTBn775YufmDcsXlTb7cO2Wzbt/eWyLsfJ+ZXWDkcXOsGajvrL805mJKmiP/IQFOYdUnp4tQbItQZsX/aEdXgC0f+KEVfuvVjYZTFYWe4zVYtRXVxzbNTc7xp+hwAvaEfUMbZYqQDopJFuiN2aboUeOfh60RfmERPYN1/lptCFp+at+KH9arzeYLdLGy3LBGhpf3jqya/bQ2OCAwLCo2Pi4MC0NbRXqkWPMpkE6gSRbojdm6/83csxl8Dp5YMzA/rHREeGhQUH+PoxCGRibW7y57OLD2kaD0WTp5r2XZS1cr031T/48uHvhxJQwrcrHLygkIjI6Nn5AQkpy/xAlBa/53oScm0EgpVfY6tJCgfFAEG9wtswOe0VPIkGIUoUnpackDIyLjYmMCNUFBfhrtSoF9PECJiRpwvvb9l+4/ayuUW/rV0zArNViMRkN+sa6Z7cv/LxncUF6hDcFXn1opdYvKCg0PCIqNm5AQnJa+uCsjBgfGoR091nkfMyAlC5gu34BgniDs6XHWpCjLwIBdezgYZkZ6SmJAwfExUaFhwUHaFVyCgRobUzGmKLlu/YdvnDjEbfa1NikNxi4ik1ms4VnNptNHINB36RvaqitfHLj/KF9O5cVjcmI8ZWDEC1TB4RExtqqTUrPGJI1dGTuiIHewnALmuzhT5SBlPaSbIlemC2EVCDH/AufiDxq5LjcnGFZQzLSkxMGxMWE+avl0CHGJyg6eei4KUULV2ze8cXXPx86cupcizPHDh0q+2bvrk0r3p8zNW9EalywDwMd8pKrdVEDElPSMzKzR+bmjs2bPLafGhx+MCPnQRhIaibJluiN2TIfo83zeGgmC86ZUjBxbG7uyKFZg9KT40PUNIhDyVvQjiL/83//BzLaC7oi84lIGJw9PCd37PhJBQUzZ02IlEOzqCcscvYqQVLUnyRbohdmS482IcewBOyY1KLimVMLJueNG507YnC/QAVIwItWqDUB/gy4QBGcOHzM+An5BdNnFs0rmT/Uuzn2EgNyLJNkIK00K8mW6H3ZQuA15FjLfWx9+U4sfX/+vKJCLtwJI/trQCKU0i8oNEonB5doE0blz+CiXfDeBx8unxpKA4DyrAU5t4NBap+QbIlemC2zFh37kkPmrl7y4aL35hXPmTkp0Rsk48XoouISEwPBRT4Jk4sXvL/ow6UrV298P4oGGFGHNlsYkBqzwUSyJXpdtlRKLXLMZTSEfLB1w9pVS5eULpybqQUpyYIGpAwZmaoAV2mGFH+4YvXajZu37l4RDdRX9rYahlAgvcRyki3R27IF1c9oU5WsWfzZ7u1bNm9Yt7wgDCTmmzRibH5BMLguZPLqzVt37P7k86/W+cU+RZsTPtATqGnbDlaYSLZEb8pWNsmMHOMnM3/86vPPPtmza02WHKQm7zehsPiDMWK6k/8/e/fh0NS993H8H3nuvf1mgmAAARdOcNGiRdy7tigWtO7Zwa3bqrUW9x5VXGC0roqrqFVcOCpOlkzDDPOQ9XnOyCEhhhDUXPTe8+o+g5P8ct4c8supDl63/7fEI0nHTy/c3gCWeaaSPEbRI8ymM0kkH3a21OEBONVXTp86eSLp6K99yAOCv1qyetOm/gpqhZ5rjp88dfrMuT8rwXnekQQSiZStKt4MliXn0uVLF85v6UqeoByyZteh86taV16nX1MupV65km0By7JKTRKJlK1Vl2xwmIe3b9082p08o+vikxfTH072otbovC/t9t2HdeAU9CaRRCJlq14jXM7KHj+6PZY8RBn9x5OC+vNh1CqRqRlPXguPbquaJBIp20bdcsExFeUekJOn9Nhb2oDauT7UKgnZhQ3g6PqRRCJla6NaZgKHKYklj5FNfQ4g7TNqldG59eCYN6pJIpGytRPyHLyqSPKcjjuNgGGlP7VG/zzwXoWSPYlEylb1gwGcuq/Ig8bnA3gcJaNWGKEDx/iTmiQSKdsmgu+C07BPSZ6jSWEAy4Zgcp/i12pwMrpSUxKJlK0irh6coijyoOnZALLHKMhtEZlmsJh5SpJIpGwd+J62gGW6HkKeozleB2B/CLmr83kGnCv+5EgikbKVRb4Gx/hHiJw8ZuIzC6CLVpNb5CFJ9eCUj5bTe7IrOfkr+oCMWXssOTmSWiMxOXkUuUO5IGm2jDhHkpOHk6OOycnJIcQZeXB1MP2PGfszO/Cff+zZknqtQej2wgA1eYrX/ioAx3uRO9SfWa+1xu1qegvB3ZroSpwCYAV9OJLAiaPWqAYWkjsmAxhNHAaYQ45CAXxGrHY1wF7635IMztcffbbU6RZ45owpGhl5yNBHFkA/w5taJNdMyzCBl9GN3kYamnj9AWY7E57MNgHAcjeyjQBwj/6nzMJ/S7byCZUQVO0IbUeeodhYDuBSP2qJz6f7qiGonqL46LJd+uDBTGrZVWDToEGD/DyTbRSAfm5kKysC1tBHoPuDB3/Qe/EXsNHlwD948EDxUWRL6s0NEJif/BDioXDD75iA+u/bkysyn57LMk0QGA+r6W2zNTM2r/6T2e5y7zD5wLdk856zpV5zu5Eb2ZJm+lD6GPQFcui9KGxpDAGoPo5sqWOaBVam9G+7tpORByx/DeBOBDVP0T50WYYRor8H0Ftne5XsfXjZ6j2arY3LbFvBb8a8eR3+G7Kt/i/KVj68CI1Md37sr1HRe9ct1QAYVwZQM9QBkeufG9GoLFb9EWa7978y26n1ACKpzYT/x7JVfETZkiq+DjamrB3jgn0U9J4tygfwZKharXDSrKbntGSdBTbMr+3Jo9mqyYl2dutlJHK5i6zJ0keOh/F2L1uFwv1TTi1762y9W8hWriZH3jKii3Carasfy5RKcpMPiZodt0VOs3X/9XM/2+Fvk626jbIln0QD7FXfWDU0yEdJ71PwOQawHIiLHhPWQdXkvNeETNr51Ah7xvOdyXPZDtyUWmF6emyRkqy0Wm0U9fy9EjU3pxKrd3KRpS59nZIaef87+aW59EpCH7KaqdUmkCr+hcGceTKUeLtP5gJ4zH6xL4mjWXu/HOw+46mJg1qtAbjPbmYtYcr+dKY+ff9kEiVqtWOI+i757egbp1yvAzpU/7W5Owm82a8SToJorXY3cXqxCwPfyNZ7VYoO+gsj7bLdp9VOJM4PWu0ydoMco/HZsS7UyPfXGzqY8hL11+Lj44PInmbJIz2YlxeiyYmIXXcZc/7mYJrBDRJnsla7k6z2iuNDFLYnh4H+0dFQsurMPvQQirqsR9k16yOfpL1pBGrYFTuItUOrFQeqP7vMl1gyfix7n65EzY04YoUeL7LU3lurIJE4rMbGge/J/kN8Sgu12lVENFB7uR7A7+waOREt02rjSeDHLgojznatdgpRj/i9WrWwZvmpHEtxys8hbZEthaSZ0YSl8uqqkZ00ahm9N3GZFsDUYKzLPTreX0Y8RbuAPrG7Mxg0ZcmIkHsu2xVGCO6Jgw1gWpdy8I6rKNoAXnpnsur/HIK6eSTYAdynA+DVziJOHkTLifWVHlaJarJTAVEcsdqfgFWyr93FWHnEDNQ7ZjtcD17ZCGs9ACaQYBWQT5zBALo5Ztv1b/Asi23ZvgaWEucEkEJ/gFfRWGKcDlZnyMGIUlilhZCj6Qx4+aHbgQfEWWN3vSwClhHvJzMExr3yxnexCI8ygnfah98Iomxi5dhmv8cC6CD+aBsbUglekpKmGMG705Hs6SH6migSQA8SHAFSiWgSGimJKAU4QYJOAIYTJwv4mXYYAfgQa0g+BJWT2iJbWVQuHFmq722fOiDwvaXre6IOVrq1ITJSevt3HfrjqTwT3lAYrSZPZau8woV2+2yWBaiMasx25h0w+Uaw1vVrgCm/DqzTJIitByzZZ+/UADhoy3YaUKYDyxj6ZrZhVYD5yVUd33/HZrPtkQtWQSFY2SG2bI8Db2a7VAe81oNlmNK6bHvrwGLyTbDMdJ7tYkBXBlZNEPE+NwLlSQl7ud5/dKjWCKAhPcsA4K6cmloGTkkZ8HCHy2w3g1V+txisNbZsR+WDyTOCtaEV2U5P558da024QXz9Tnok2x2ANdvvjID5xen0egC/tEG2pIgrgxPmwtSEaeEd/bxV795uYHINRExCaI+R3yWm6+GM/od25LFs4wHzFhURDS8AHjdmm/N4nIzUCxmg5nrJXDXJhr4CLAOFOMqAjHAi8kkCMEbMtrQyIZgo8Ih4OeoXEaEDdkdERAQR0SngVV8i6rnKiOs+ZBMeEVEDbGQ30xDReYBZpSHSrGGA043nVxJQlH7xjEO2RaXxPkRdd1uAXEWrsk0GcHWAnJTRGUVOsy3Qr/In6vIHxDunlJnAFV9ifQuYIsneQyBtgjdR8AmIDTYKqAUaVgYRdfilrshVtiFGYM9AGVvQM8DYrzHbnL+GEam+0QN1nYgoOCJiP1DIDldfV9nmPOJev28ZoOqGbraKZMPzAcun1HTga4EEYeCdZKuJiFgGIJLdgFxkqzVBd//SBTVRVwa43YOIOlwAzP3bIFtSLa1ubNXSNN2SW4k/TAgL9vNVy+ntKVbo7Mu886LK4TAGWNVtbE/vlu3DUTYO2bYvAxYTr7cBmNmYrT9xlgAwCLH2tQDbiLMJyFQT7yzwUMwW84l3CbB42x1GkAfEEm/cCXWzU1IjActw4o0GMExcjWv2oYjZ6vsTby6Ama3JtrsROEo83wyn2eLLxiL1xBkIIJh414B1ZK97VroPceQZb3yPTADMw4g3weQqW4phNhMvrAGIb8z2pkpYC3GhbW8X2WZpiLMcQIMQaziATc1MSTnNljVFnJJykS3SR9huUb0jI95d4GJbZEteW+vAM+kKqwxwwBTcPrI6NrJnkL+vl1ouo1aT971nRrMsTMUL8R0ukxhI75itPYeefgEy5LaPazLFbDcQrx1ju+HvuvUqGswA40jQ0wJMtGZbI7f11s8xWy8Ag1r+ACgdSCKrE8BtcfUjNTnJ9qDdc8xoTba7gcoAEkx3mm2e3Vp+ywVAIQk2v3FOBvmTYAWgI3tqPZBIVjddZkuhZHUbSGzMdoYt0J3uZ7ueeL4G4JZtkH73QLa5viToa4ElnATDAHzaFtmS5jAjZPtk8dpruuoGJ3FVZt5IWr9wUmRo18AAjY+3WiWXkysyuVLt5dM+ILBz6KANOjTDzFRmn1k07Z5RqPZsV/JgttfEQllx4mUSwHQSZANHSLAP+JtYE4E6GVkVAmus2T4kQTCAaMdsSbxz0GW2ajMwiaxiAaNSWO1s12pxJ9Z3gEnhZraOZ3Afp9mKXYaLn/UkAM9sX/kJOTcFQDvHT4VHk9VOl9naHAOuN2Yrnv0XgBT3s40jQZ7tu8ZB4L4Hst1NVguAXLKSNwBz2yRbCjpt7fbhCM3wdVfyK+tNcMaoL3iYqt3508K4L4Z9GhrSMTgoIMBfo9G05/i2Z//NLyAgMDi4a6/+g8fFzFm6MflS+qtKI5yxNFSXZiTP79suylptw5+hsnfOtvaljUNPBbZC6VMA4eJMsu0CcZgEa6wdLBYL5fwFJDXOJFsZgHlvZHsGMO6NUrnOtr94oRaLCRNXu/7McZRQptvZ5gPryaq5mWRBAICJ1u8MVSTYD5wg5yYCaE92xgPoTFbb3cz2AHDTNpNMgt+Au+5nG2u7cB8kwS9Argey/ZmstgIpJMoENrVNttTlnNCtOTO2HXmHLzj6SKdnTGiWhdGXFGQ9TEs9d/Lo/j1b1vMSduzff/jE2Ss30l/kv66sc7W7oa4iN3VDdGcZeUc/FzY03OgjVuuRKSlvC5D/wiobQJxDtldt2a60drAfqH0hqgHuOWZb7yzb7pVgGV4mze7cfLZfA9CQVSCAaPey7QtgpPvZKkzA9+5l217MNhzAKOJ4Zb8ZGo34YcPR8ykp6Y7ZLgKgcDdbr9hl20+mpKTkO8t2z1tle8OW7RoPZ3se0L8QMcDZNsqWnUhkhKCKF7YnIlnQiGXJD4sr6xrMeO9qy3Ku7Z7VV02s9nPzLeA03I6Qkyez7QsH8S1n+yeaeuVWtjTkGQTGrd7NZbvCPtsAAEvcz3am+9l2BfBdK7OlVKA4htv+MlDoT00seA6RQ7YJAOTuZavZUgHRx5jtEzR1u62ypc7WblH5S6CMeAGD5+++9EJXWcMYLXh/6g/GiP+boMz/p3LwmJtitZ7KNgiAdr2dyJazPQ7krrez2L1sST79WA54qc1lO9cx25nuZ/ul+9n6vE22vqkAmOxyAOZRJBJHBob7F09otTcds13qdrbKGwAqbp3VarXZH2W2fwEZ6+3Mb7NsqfPZemtWSb1UJFJ1GTZv07n0vFIuXvPb1euw15NPyUrZI7HOWu2lPjLyXLZiMV+TI9fZ/gzcIXtuZCvSRC3PBTC1mWyHAAghq54APncv288B9LNm+0XL2VI5sKy12ZLyGASOvzb9EAuQFOj8ve0UcYFjtrmO2W4ETAtUxDnQqmzXvq9se7aU7UmX2R4ATpO9tsuWgrW14BlujmxHTfiFTfh+S/L1jDxdub6mjjGa3OnXYjY21Nfoy3V5OgNsGjYGk8B72PUG8OrOhMrI09neB9a0MttpQMVbZCvy0QMHmsk2AMAY+9kcjXvZzrDepqO2ALPdyPYhkNjqbAen6PccO7v7u2HkYB2gk5HzbCMADHoj26VAtWO2j8TH1KpsnwB73ke2AwBEucz2d+C6y2yXAk8/lGwpeF8VeOa8b/2cZKQO7DcyLn5j4umrD14W6MrKK/RVVbW1dUw90yBgGKa+trZar68oLyspznmclnJky5Jvxgz4+m8TRMa0gTLiyDTzss3gVR/qSuTxbI8BqST6zK1sw+2newNC3M1WTlaHgNRmsiUdsJJsp1khucz2V7LaD5QQp9h2Gu1xke1p4KmMBKPdzDYRRRpy6gKQ6nhxFflZ7G6F/F3MNgaAP/HUjJCtygD8QIJj7mebYnuos94l23Z2r/mfTrPdZDvqIKfZTgAagsmqa4e2zZZ8/51t7av+1AgNNUvt3yNy7MTJsbMW/bD8p4Qt2347xDu4a8uWX1cv/2HBjNhJE0YN+ax7ezkJYm6KN0bp/xyuIo7P8OR68MyvVmjoP5BtuBn4Uhx34wl1C9mKy66TwPueLtK9bL/KH2b7JGNfc9kuBSoDiRdcBfzbdbbP1MTrYwA2iU82Uy7ejNh8thMAzCfBYfey7Q/sJedOAX+TYLtjtnQOKPMjXlCFmG04Gu9N+xJCtrI624dSWe5nuwNgOhLv9LtkSzrgMvH8apxmuwDAEOKtd5qt7AmQTIKAzNywts2WvKY9YMAzPvm+m5Kc+0QV2DusW7Cft0JBn/zrH//8178++eQTIvZv//rXP//5j39+QkovTWDX0L6d231CPNmII09Lq+tqSp7vjVARS9555iMjeMzfs73ovWWbFmwTRJxXjT0dBcrjiDO2FkhoKVsx9WQNsQIuAmVezWeb23iYBWYw3xCnZ6WYjJNsVTnA3VBihaUDmUrX2ZovdiLWkFygXnheGwBsU3C5/W56M9taMVt6BNTwD8J7g9m9bMcBzIIhPbp3795FQU2tBUzCCT2p7o1sBwG4LzyjR2YxW3UJUDqYWJGlFiFbegBk8bvK9xpcZ7vSLtsYABc0RKRca3qnbE+L03TBt02N2UbbZdvHDDwOIdaMBqfZ0ngAu7yI1Yl78WRtmy2pRlyohEB/ZESA82q9eg4dMnAAe7eFf/t2ShnXK4uEbj8hmdLL1y+wa+/+EVHDBnhZu6WAib8kJiWuGeNDHM3gnUXiYa6MU9G7cf1LwF0DSjMzOxNRZz2Ae/tWnCoD8ErjTraUCEB36udtf1sghuA821T+MJm9iMJyARSfiv9+fy2Q6dNctjTZAjAXN2+5xACWr8h1trtMTMq2fWkmNN73p6kA8OLQxhNV9U5+SH4C6DIzvbjDgPXk2MaTOmxzL1tfI0Tm4ssjyE5fBmBSl8YsvoFV6UBWZgjZuQyg/tqO3anmiiNitvQdAOPlbbvTcFx8b7sEQOnxmd+syzNPMKI+81Kz2X4NGNhRXS3eDopi7YbDBdjyTtmGGgHc37/lHFN4ojHbPgDYIx0Rv8FDf2bjgWfY4jxbuggg//jqPc8BWMZTG2dLst57Ci3gmZ4tD/WmNynDYmK+GBUV0bdHR39vlVxGYprEp8tm2z6oW5/PokaMnzwlzC5JlZeSeF49Ft1hICg+2EdOHs12GiCezD3uQ/SwO7mVrWyNCVbMImouW7EO4Z1w4B2IqvpTs9nS+BJY6cZSC9kuTIDVXgUJZpsgWOxkSurf4HgTaw4Dwc2+7mVL02pg5zDZWQarjHYzwOpFdnxTILDM3t6YreIWBMUhYraym7DaSacAvGg2W+98cHYTJ1IPwfGx75Qt/QqBKdo2JUU3AYhvsrrkQ3C3RzPZqnZCVBVLbZityO/HpwwEzOVpIao3wu4yI37e9CkTR0eGBnk7TU7GvvcdOPKLydPmfDe3m/yN6jtFayshYJ4t8yfybLY07ykjnszKzfngZC5TkVvZsoalM2DVJw8gV9nSzAzGmi2pN74Gh9kVRC6ypeAzerD0p4OoxWzpx1KwChZSo6hcsEoWkZNsZUteGqzZUtQTMwDmgNrdmWSfFEsFrxacWWQnhj+o8YSGFBvzLdZsRbKEUrDyYsmWLSkSTGBdDyMxW/L6uQ6s8pXc/QKVLrKlnimljdlSyC2w6rcq3jFbiikHKyea7LLtcKSwMVvSnALLnKR5cyZZFP3YyEd7oAd9CNmSatz5UggspYfGdVJQE75fblm/etn3c2IGdlBQM+S+fSbM+m7J6vVbv9Y4rAkauf2VCYKyyxPV9B8WOH7BxH7UKsrP5kwd0p5aQz5o8sKYcB9qUc+42B7kHvXoudHB1ESPmDnDvKllvqMWTAwkd80vYWJI0G5KEXCd7Cn6RC/4shM1p1vczKFyErIVeUfOjenj8IgGfTNntBe1Vsfx88d0oHcn7zN1RqSSXPAfOf+LLuSSOnJ+7GBvog8jW5KFJGQxEKd5t43saJ/nJ903nzp2YNempUN9yQWvfvM37PjtyKkdoU2jjVr3pAECJmdzTxl9aCSfW3CcGk0Cyqn1bNlKPJ6tSB1zocQCgSlry4guahIpR1y/d+vqpeMzfVqa3NpzKTXtXtpYu12Do9b+zcCq9HKcN314JD8Ba+wiBvKkbD+ObEnWbd2TaliZsvd8GSIm5v1Nsa44P+9iOLWg48ZXBcUlr+f5ksCr65iNGQys6l6u7y6jD5BkBXBPbt/fCSnbjyBb8Wp5LI+xfQ5waGpYgIJYvt+ZzSaT4U4YtSBwB2M2m7FUQyy5X2jsgWwDrAyFpyep6IMkiQRwfqCaWL4jkgFMkbL9CLIV+c26XGyElaUiddmI7r5yUk+tB6sw3odcUo/7C6yGeT4k8wkZtvhSmRlWJt1f8Z1l9IGS7AdHX17OgLOEpGw/omxJ1nXJDZ0JIkN20qKobv7DXvLpPZ7agVzwGf47n/er0X4hn889/LIBIlPFvZ/7KenDJVn2EiLDH1+RlO1Hkq1IHrr6ts6IRsyL5PiZV4T6Mlf08aVmqEOmpTLg3Jy+6FBGHRqZyu5vHqSmD5skfO6yzYm71yyYFEBvp8vAgWHUJqRsWYq+P90oYGBjKBbvoqq5NH9AsJMClX6hMYmFEBTlN8CmQXd302AvEkkkUraeIe/1Q0pONZypTUuICe/m5yW3+119Ovcdt+z8awucqMtP/Slc7FwikbL1qOC4Q8/KDHDCXJF+ZOk3Yz8P78saMGhk3OI91wqdbmksf/n7gp4KEkkkUrYe5j0qITVbb4Izllpd1oO069dvpL8sqna+ianq1V9bx/uT50kkUrY2ipDY3TdzKhvQagZ97p1907sryfMkEilbB6qesVv/fF5cY4LbTPW6l1d3TQ9VUVuQSKRsWfKOoxYfvvmySN9gRgssptqSnNvJK8d3UVDbkEikbEWqbuN+3HMp/WVBWbXzeC2GOn1xdsb1Y2vjentR25JIpGxFcr9+4xclHDyf9vB5Zl7h65LSCr2+srREV5SX/SLj3pXk7ctiI7uo6UMhkUjZiuSazv2HR8/+ftnadZt37tq6fu3KH+Z+PW5Qj0DpGvv/7dOxAAAAAMAgf+tJ7CyHYGwLaAtoC9oC2gLagraAtoC2gLagLaAtoC1oC2gLaAvaAtoC2oK2gLaAtqAtoC2gLWgLaAtoC2gL2gLaAtqCtoC2gLagLaAtoC1oC2gLaAvaAtoC2gLagraAtoC2oC2gLaAtaAtoC2gL2gLaAtqCtoC2gLZAy1EI1wwY3vcAAAAASUVORK5CYII="
+    }
+   },
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<img src=\"../../../../images/qiskit_header.png\" alt=\"Note: In order for images to show up in this jupyter notebook you need to select File => Trusted Notebook\" align=\"middle\">"
+    "![qiskit_header.png](attachment:qiskit_header.png)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# _*Qiskit Chemistry: Computing a Molecule's Dissociation Profile Using the Variational Quantum Eigensolver (VQE) Algorithm*_ \n",
-    "\n",
-    "The latest version of this notebook is available on https://github.com/qiskit/qiskit-tutorials.\n",
-    "\n",
-    "***\n",
-    "### Contributors\n",
-    "Antonio Mezzacapo<sup>[1]</sup>, Richard Chen<sup>[1]</sup>, Marco Pistoia<sup>[1]</sup>, Shaohan Hu<sup>[1]</sup>, Peng Liu<sup>[1]</sup>, Stephen Wood<sup>[1]</sup>, Jay Gambetta<sup>[1]</sup>\n",
-    "### Affiliation\n",
-    "- <sup>[1]</sup>IBMQ"
+    "# Qiskit Chemistry: Computing a Molecule's Dissociation Profile Using the Variational Quantum Eigensolver (VQE) Algorithm"
    ]
   },
   {
@@ -108,7 +105,12 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:21:04.043864Z",
+     "start_time": "2019-12-12T17:21:00.027902Z"
+    }
+   },
    "outputs": [],
    "source": [
     "# useful additional packages \n",
@@ -140,7 +142,12 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:21:04.076064Z",
+     "start_time": "2019-12-12T17:21:04.073379Z"
+    }
+   },
    "outputs": [],
    "source": [
     "# from qiskit import IBMQ\n",
@@ -161,34 +168,39 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:21:25.444603Z",
+     "start_time": "2019-12-12T17:21:04.103881Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
       "Ground state energy (classical): -1.137306035753\n",
-      "Ground state energy (quantum)  : -1.137304610765\n",
+      "Ground state energy (quantum)  : -1.137075264791\n",
       "====================================================\n",
       "=== GROUND STATE ENERGY ===\n",
       " \n",
-      "* Electronic ground state energy (Hartree): -1.857273602044\n",
-      "  - computed part:      -1.857273602044\n",
+      "* Electronic ground state energy (Hartree): -1.85704425607\n",
+      "  - computed part:      -1.85704425607\n",
       "  - frozen energy part: 0.0\n",
       "  - particle hole part: 0.0\n",
       "~ Nuclear repulsion energy (Hartree): 0.719968991279\n",
-      "> Total ground state energy (Hartree): -1.137304610765\n",
+      "> Total ground state energy (Hartree): -1.137075264791\n",
       "  Measured:: Num particles: 2.000, S: 0.000, M: 0.00000\n",
       " \n",
       "=== DIPOLE MOMENT ===\n",
       " \n",
-      "* Electronic dipole moment (a.u.): [0.0  0.0  0.00070479]\n",
-      "  - computed part:      [0.0  0.0  0.00070479]\n",
+      "* Electronic dipole moment (a.u.): [0.0  0.0  -0.00502979]\n",
+      "  - computed part:      [0.0  0.0  -0.00502979]\n",
       "  - frozen energy part: [0.0  0.0  0.0]\n",
       "  - particle hole part: [0.0  0.0  0.0]\n",
       "~ Nuclear dipole moment (a.u.): [0.0  0.0  0.0]\n",
-      "> Dipole moment (a.u.): [0.0  0.0  0.00070479]  Total: 0.00070479\n",
-      "               (debye): [0.0  0.0  0.0017914]  Total: 0.0017914\n"
+      "> Dipole moment (a.u.): [0.0  0.0  -0.00502979]  Total: 0.00502979\n",
+      "               (debye): [0.0  0.0  -0.01278446]  Total: 0.01278446\n"
      ]
     }
    ],
@@ -232,8 +244,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
+   "execution_count": 4,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T18:09:35.751620Z",
+     "start_time": "2019-12-12T17:21:25.469374Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -302,12 +319,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
+   "execution_count": 5,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T18:09:35.952844Z",
+     "start_time": "2019-12-12T18:09:35.774163Z"
+    }
+   },
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -329,14 +351,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
+   "execution_count": 6,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T18:09:36.039781Z",
+     "start_time": "2019-12-12T18:09:35.970951Z"
+    }
+   },
    "outputs": [
     {
      "data": {
       "text/html": [
-       "<h3>Version Information</h3><table><tr><th>Qiskit Software</th><th>Version</th></tr><tr><td>Qiskit</td><td>0.12.0</td></tr><tr><td>Terra</td><td>0.9.0</td></tr><tr><td>Aer</td><td>0.3.0</td></tr><tr><td>Ignis</td><td>0.2.0</td></tr><tr><td>Aqua</td><td>0.6.0</td></tr><tr><td>IBM Q Provider</td><td>0.3.2</td></tr><tr><th>System information</th></tr><tr><td>Python</td><td>3.7.4 (default, Jul 16 2019, 07:12:58) \n",
-       "[GCC 9.1.0]</td></tr><tr><td>OS</td><td>Linux</td></tr><tr><td>CPUs</td><td>4</td></tr><tr><td>Memory (Gb)</td><td>15.560554504394531</td></tr><tr><td colspan='2'>Fri Sep 20 18:30:29 2019 CEST</td></tr></table>"
+       "<h3>Version Information</h3><table><tr><th>Qiskit Software</th><th>Version</th></tr><tr><td>Qiskit</td><td>0.14.0</td></tr><tr><td>Terra</td><td>0.11.0</td></tr><tr><td>Aer</td><td>0.3.4</td></tr><tr><td>Ignis</td><td>0.2.0</td></tr><tr><td>Aqua</td><td>0.6.1</td></tr><tr><td>IBM Q Provider</td><td>0.4.4</td></tr><tr><th>System information</th></tr><tr><td>Python</td><td>3.7.5 (default, Oct 25 2019, 10:52:18) \n",
+       "[Clang 4.0.1 (tags/RELEASE_401/final)]</td></tr><tr><td>OS</td><td>Darwin</td></tr><tr><td>CPUs</td><td>4</td></tr><tr><td>Memory (Gb)</td><td>16.0</td></tr><tr><td colspan='2'>Thu Dec 12 13:09:36 2019 EST</td></tr></table>"
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -363,6 +390,13 @@
     "%qiskit_version_table\n",
     "%qiskit_copyright"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
@@ -382,7 +416,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.3"
+   "version": "3.7.5"
   },
   "varInspector": {
    "cols": {
diff --git a/qiskit/advanced/aqua/chemistry/index.ipynb b/qiskit/advanced/aqua/chemistry/index.ipynb
index 220c8ce7d..6a53c6eca 100644
--- a/qiskit/advanced/aqua/chemistry/index.ipynb
+++ b/qiskit/advanced/aqua/chemistry/index.ipynb
@@ -1,10 +1,15 @@
 {
  "cells": [
   {
+   "attachments": {
+    "qiskit_header.png": {
+     "image/png": 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"
+    }
+   },
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<img src=\"../../../../images/qiskit_header.png\" alt=\"Note: In order for images to show up in this jupyter notebook you need to select File => Trusted Notebook\" align=\"middle\">"
+    "![qiskit_header.png](attachment:qiskit_header.png)"
    ]
   },
   {
@@ -49,7 +54,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.3"
+   "version": "3.7.5"
   },
   "varInspector": {
    "cols": {
diff --git a/qiskit/advanced/aqua/chemistry/programmatic_approach.ipynb b/qiskit/advanced/aqua/chemistry/programmatic_approach.ipynb
index bde446cae..5325b0a0c 100644
--- a/qiskit/advanced/aqua/chemistry/programmatic_approach.ipynb
+++ b/qiskit/advanced/aqua/chemistry/programmatic_approach.ipynb
@@ -1,25 +1,22 @@
 {
  "cells": [
   {
+   "attachments": {
+    "qiskit_header.png": {
+     "image/png": 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"
+    }
+   },
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<img src=\"../../../../images/qiskit_header.png\" alt=\"Note: In order for images to show up in this jupyter notebook you need to select File => Trusted Notebook\" align=\"middle\">"
+    "![qiskit_header.png](attachment:qiskit_header.png)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# _*Qiskit Chemistry, Programmatic Approach*_ \n",
-    "\n",
-    "The latest version of this notebook is available on https://github.com/Qiskit/qiskit-tutorial.\n",
-    "\n",
-    "***\n",
-    "### Contributors\n",
-    "Richard Chen<sup>[1]</sup>, Antonio Mezzacapo<sup>[1]</sup>, Marco Pistoia<sup>[1]</sup>, Stephen Wood<sup>[1]</sup>\n",
-    "### Affiliation\n",
-    "- <sup>[1]</sup>IBMQ"
+    "# Qiskit Chemistry, Programmatic Approach\n"
    ]
   },
   {
@@ -39,7 +36,12 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:23:11.199973Z",
+     "start_time": "2019-12-12T17:23:06.623100Z"
+    }
+   },
    "outputs": [],
    "source": [
     "# import common packages\n",
@@ -72,7 +74,12 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:23:11.422350Z",
+     "start_time": "2019-12-12T17:23:11.212313Z"
+    }
+   },
    "outputs": [],
    "source": [
     "# using driver to get fermionic Hamiltonian\n",
@@ -94,7 +101,12 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:23:11.467380Z",
+     "start_time": "2019-12-12T17:23:11.436288Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -126,112 +138,117 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:23:12.226887Z",
+     "start_time": "2019-12-12T17:23:11.507225Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "IIII\t(-0.2076593350197074+0j)\n",
-      "IIIZ\t(-0.09376337484626278+0j)\n",
-      "IIZX\t(-0.0031775814548399956+0j)\n",
-      "IIIX\t(0.0031775814548399956+0j)\n",
-      "IIXX\t(-0.001251396599969751+0j)\n",
-      "IIYY\t(0.001251396599969751+0j)\n",
-      "IIZZ\t(-0.21162509515110128+0j)\n",
-      "IIXZ\t(0.019200533863109957+0j)\n",
-      "IIXI\t(0.019200533863109957+0j)\n",
-      "IIZI\t(0.3581026994577025+0j)\n",
-      "IZII\t(0.09376337484626282+0j)\n",
-      "ZXII\t(0.0031775814548399973+0j)\n",
-      "IXII\t(0.0031775814548399973+0j)\n",
-      "XXII\t(-0.0012513965999697415+0j)\n",
-      "YYII\t(0.0012513965999697415+0j)\n",
-      "ZZII\t(-0.21162509515110128+0j)\n",
-      "XZII\t(-0.019200533863109957+0j)\n",
-      "XIII\t(0.019200533863109957+0j)\n",
-      "ZIII\t(-0.3581026994577025+0j)\n",
-      "IZIZ\t(-0.12182774215821467+0j)\n",
-      "IZZX\t(0.012144897228064444+0j)\n",
-      "IZIX\t(-0.012144897228064444+0j)\n",
-      "IZXX\t(0.03169874598734439+0j)\n",
-      "IZYY\t(-0.03169874598734439+0j)\n",
-      "IXIZ\t(0.012144897228064444+0j)\n",
-      "ZXIZ\t(0.012144897228064444+0j)\n",
-      "IXZX\t(-0.0032659954996582014+0j)\n",
-      "ZXZX\t(-0.0032659954996582014+0j)\n",
-      "IXIX\t(0.0032659954996582014+0j)\n",
-      "ZXIX\t(0.0032659954996582014+0j)\n",
-      "IXXX\t(-0.008650156860609944+0j)\n",
-      "ZXXX\t(-0.008650156860609944+0j)\n",
-      "IXYY\t(0.008650156860609944+0j)\n",
-      "ZXYY\t(0.008650156860609944+0j)\n",
+      "IIII\t(-0.2076593350197093+0j)\n",
+      "IIIZ\t(-0.09376337484626251+0j)\n",
+      "IIZX\t(-0.0031775814548396244+0j)\n",
+      "IIIX\t(0.0031775814548396244+0j)\n",
+      "IIXX\t(-0.0012513965999699584+0j)\n",
+      "IIYY\t(0.0012513965999699584+0j)\n",
+      "IIZZ\t(-0.21162509515110095+0j)\n",
+      "IIXZ\t(0.01920053386310993+0j)\n",
+      "IIXI\t(0.01920053386310993+0j)\n",
+      "IIZI\t(0.358102699457702+0j)\n",
+      "IZII\t(0.09376337484626249+0j)\n",
+      "ZXII\t(0.003177581454839621+0j)\n",
+      "IXII\t(0.003177581454839621+0j)\n",
+      "XXII\t(-0.0012513965999699714+0j)\n",
+      "YYII\t(0.0012513965999699714+0j)\n",
+      "ZZII\t(-0.21162509515110095+0j)\n",
+      "XZII\t(-0.019200533863109923+0j)\n",
+      "XIII\t(0.019200533863109923+0j)\n",
+      "ZIII\t(-0.358102699457702+0j)\n",
+      "IZIZ\t(-0.12182774215821472+0j)\n",
+      "IZZX\t(0.012144897228064262+0j)\n",
+      "IZIX\t(-0.012144897228064262+0j)\n",
+      "IZXX\t(0.03169874598734438+0j)\n",
+      "IZYY\t(-0.03169874598734438+0j)\n",
+      "IXIZ\t(0.012144897228064262+0j)\n",
+      "ZXIZ\t(0.012144897228064262+0j)\n",
+      "IXZX\t(-0.003265995499658126+0j)\n",
+      "ZXZX\t(-0.003265995499658126+0j)\n",
+      "IXIX\t(0.003265995499658126+0j)\n",
+      "ZXIX\t(0.003265995499658126+0j)\n",
+      "IXXX\t(-0.00865015686060984+0j)\n",
+      "ZXXX\t(-0.00865015686060984+0j)\n",
+      "IXYY\t(0.00865015686060984+0j)\n",
+      "ZXYY\t(0.00865015686060984+0j)\n",
       "YYIZ\t(0.03169874598734438+0j)\n",
       "XXIZ\t(-0.03169874598734438+0j)\n",
-      "YYZX\t(-0.008650156860609944+0j)\n",
-      "XXZX\t(0.008650156860609944+0j)\n",
-      "YYIX\t(0.008650156860609944+0j)\n",
-      "XXIX\t(-0.008650156860609944+0j)\n",
-      "YYXX\t(-0.030981613344629504+0j)\n",
-      "XXXX\t(0.030981613344629504+0j)\n",
-      "YYYY\t(0.030981613344629504+0j)\n",
-      "XXYY\t(-0.030981613344629504+0j)\n",
-      "ZZIZ\t(0.055902510785159+0j)\n",
-      "ZZZX\t(0.0018710427514161882+0j)\n",
-      "ZZIX\t(-0.0018710427514161882+0j)\n",
-      "ZZXX\t(0.003104004116073226+0j)\n",
-      "ZZYY\t(-0.003104004116073226+0j)\n",
-      "XIIZ\t(0.012841723180756926+0j)\n",
-      "XZIZ\t(-0.012841723180756926+0j)\n",
-      "XIZX\t(-0.0023521521732445616+0j)\n",
-      "XZZX\t(0.0023521521732445616+0j)\n",
-      "XIIX\t(0.0023521521732445616+0j)\n",
-      "XZIX\t(-0.0023521521732445616+0j)\n",
-      "XIXX\t(-0.00797590875056439+0j)\n",
-      "XZXX\t(0.00797590875056439+0j)\n",
-      "XIYY\t(0.00797590875056439+0j)\n",
-      "XZYY\t(-0.00797590875056439+0j)\n",
-      "ZIIZ\t(0.1134611071268544+0j)\n",
-      "ZIZX\t(-0.010838363828740441+0j)\n",
-      "ZIIX\t(0.010838363828740441+0j)\n",
-      "ZIXX\t(-0.03355135311123841+0j)\n",
-      "ZIYY\t(0.03355135311123841+0j)\n",
-      "IZZZ\t(-0.055902510785159+0j)\n",
-      "IZXZ\t(-0.012841723180756926+0j)\n",
-      "IZXI\t(-0.012841723180756926+0j)\n",
-      "IXZZ\t(-0.001871042751416188+0j)\n",
-      "ZXZZ\t(-0.001871042751416188+0j)\n",
-      "IXXZ\t(0.002352152173244561+0j)\n",
-      "ZXXZ\t(0.002352152173244561+0j)\n",
-      "IXXI\t(0.002352152173244561+0j)\n",
-      "ZXXI\t(0.002352152173244561+0j)\n",
-      "YYZZ\t(-0.003104004116073226+0j)\n",
-      "XXZZ\t(0.003104004116073226+0j)\n",
-      "YYXZ\t(0.00797590875056439+0j)\n",
-      "XXXZ\t(-0.00797590875056439+0j)\n",
-      "YYXI\t(0.00797590875056439+0j)\n",
-      "XXXI\t(-0.00797590875056439+0j)\n",
-      "ZZZZ\t(0.08447056807294587+0j)\n",
-      "ZZXZ\t(-0.008994911953942185+0j)\n",
-      "ZZXI\t(-0.008994911953942185+0j)\n",
-      "XIZZ\t(-0.008994911953942183+0j)\n",
-      "XZZZ\t(0.008994911953942183+0j)\n",
-      "XIXZ\t(0.006612047066151987+0j)\n",
-      "XZXZ\t(-0.006612047066151987+0j)\n",
-      "XIXI\t(0.006612047066151987+0j)\n",
-      "XZXI\t(-0.006612047066151987+0j)\n",
-      "ZIZZ\t(0.060358912810781123+0j)\n",
-      "ZIXZ\t(0.011019231644712395+0j)\n",
-      "ZIXI\t(0.011019231644712395+0j)\n",
-      "IZZI\t(0.1134611071268544+0j)\n",
-      "IXZI\t(-0.01083836382874044+0j)\n",
-      "ZXZI\t(-0.01083836382874044+0j)\n",
-      "YYZI\t(-0.03355135311123841+0j)\n",
-      "XXZI\t(0.03355135311123841+0j)\n",
-      "ZZZI\t(-0.060358912810781123+0j)\n",
-      "XIZI\t(-0.011019231644712394+0j)\n",
-      "XZZI\t(0.011019231644712394+0j)\n",
-      "ZIZI\t(-0.11344680300367244+0j)\n",
+      "YYZX\t(-0.00865015686060984+0j)\n",
+      "XXZX\t(0.00865015686060984+0j)\n",
+      "YYIX\t(0.00865015686060984+0j)\n",
+      "XXIX\t(-0.00865015686060984+0j)\n",
+      "YYXX\t(-0.030981613344629486+0j)\n",
+      "XXXX\t(0.030981613344629486+0j)\n",
+      "YYYY\t(0.030981613344629486+0j)\n",
+      "XXYY\t(-0.030981613344629486+0j)\n",
+      "ZZIZ\t(0.05590251078515892+0j)\n",
+      "ZZZX\t(0.001871042751416111+0j)\n",
+      "ZZIX\t(-0.001871042751416111+0j)\n",
+      "ZZXX\t(0.0031040041160733172+0j)\n",
+      "ZZYY\t(-0.0031040041160733172+0j)\n",
+      "XIIZ\t(0.01284172318075686+0j)\n",
+      "XZIZ\t(-0.01284172318075686+0j)\n",
+      "XIZX\t(-0.002352152173244469+0j)\n",
+      "XZZX\t(0.002352152173244469+0j)\n",
+      "XIIX\t(0.002352152173244469+0j)\n",
+      "XZIX\t(-0.002352152173244469+0j)\n",
+      "XIXX\t(-0.007975908750564321+0j)\n",
+      "XZXX\t(0.007975908750564321+0j)\n",
+      "XIYY\t(0.007975908750564321+0j)\n",
+      "XZYY\t(-0.007975908750564321+0j)\n",
+      "ZIIZ\t(0.11346110712685431+0j)\n",
+      "ZIZX\t(-0.010838363828740288+0j)\n",
+      "ZIIX\t(0.010838363828740288+0j)\n",
+      "ZIXX\t(-0.03355135311123839+0j)\n",
+      "ZIYY\t(0.03355135311123839+0j)\n",
+      "IZZZ\t(-0.05590251078515892+0j)\n",
+      "IZXZ\t(-0.012841723180756857+0j)\n",
+      "IZXI\t(-0.012841723180756857+0j)\n",
+      "IXZZ\t(-0.001871042751416111+0j)\n",
+      "ZXZZ\t(-0.001871042751416111+0j)\n",
+      "IXXZ\t(0.0023521521732444693+0j)\n",
+      "ZXXZ\t(0.0023521521732444693+0j)\n",
+      "IXXI\t(0.0023521521732444693+0j)\n",
+      "ZXXI\t(0.0023521521732444693+0j)\n",
+      "YYZZ\t(-0.0031040041160733172+0j)\n",
+      "XXZZ\t(0.0031040041160733172+0j)\n",
+      "YYXZ\t(0.00797590875056432+0j)\n",
+      "XXXZ\t(-0.00797590875056432+0j)\n",
+      "YYXI\t(0.00797590875056432+0j)\n",
+      "XXXI\t(-0.00797590875056432+0j)\n",
+      "ZZZZ\t(0.08447056807294584+0j)\n",
+      "ZZXZ\t(-0.008994911953942171+0j)\n",
+      "ZZXI\t(-0.008994911953942171+0j)\n",
+      "XIZZ\t(-0.00899491195394217+0j)\n",
+      "XZZZ\t(0.00899491195394217+0j)\n",
+      "XIXZ\t(0.006612047066151932+0j)\n",
+      "XZXZ\t(-0.006612047066151932+0j)\n",
+      "XIXI\t(0.006612047066151932+0j)\n",
+      "XZXI\t(-0.006612047066151932+0j)\n",
+      "ZIZZ\t(0.060358912810780964+0j)\n",
+      "ZIXZ\t(0.011019231644712297+0j)\n",
+      "ZIXI\t(0.011019231644712297+0j)\n",
+      "IZZI\t(0.11346110712685431+0j)\n",
+      "IXZI\t(-0.010838363828740288+0j)\n",
+      "ZXZI\t(-0.010838363828740288+0j)\n",
+      "YYZI\t(-0.03355135311123839+0j)\n",
+      "XXZI\t(0.03355135311123839+0j)\n",
+      "ZZZI\t(-0.060358912810780964+0j)\n",
+      "XIZI\t(-0.011019231644712297+0j)\n",
+      "XZZI\t(0.011019231644712297+0j)\n",
+      "ZIZI\t(-0.11344680300367214+0j)\n",
       "\n",
       "Representation: paulis, qubits: 4, size: 100\n"
      ]
@@ -279,7 +296,12 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:23:12.317319Z",
+     "start_time": "2019-12-12T17:23:12.263713Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -323,7 +345,12 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:23:17.313239Z",
+     "start_time": "2019-12-12T17:23:17.307218Z"
+    }
+   },
    "outputs": [],
    "source": [
     "# from qiskit import IBMQ\n",
@@ -333,7 +360,12 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:23:17.646524Z",
+     "start_time": "2019-12-12T17:23:17.641892Z"
+    }
+   },
    "outputs": [],
    "source": [
     "backend = Aer.get_backend('statevector_simulator')"
@@ -343,6 +375,10 @@
    "cell_type": "code",
    "execution_count": 8,
    "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:23:18.245413Z",
+     "start_time": "2019-12-12T17:23:18.131200Z"
+    },
     "scrolled": true
    },
    "outputs": [],
@@ -377,9 +413,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 9,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:24:35.402022Z",
+     "start_time": "2019-12-12T17:23:19.402168Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The computed ground state energy is: -1.077059737908\n",
+      "The total ground state energy is: -7.881072036205\n",
+      "Parameters: [ 0.03425824  0.0052999   0.03442233  0.00533808 -0.03878529  0.06029099\n",
+      "  0.06050635 -0.11631612]\n"
+     ]
+    }
+   ],
    "source": [
     "results = vqe.run(quantum_instance)\n",
     "print('The computed ground state energy is: {:.12f}'.format(results['eigvals'][0]))\n",
@@ -389,19 +441,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 10,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2019-08-22T01:50:30.121573Z",
-     "start_time": "2019-08-22T01:50:30.112104Z"
+     "end_time": "2019-12-12T17:24:35.561177Z",
+     "start_time": "2019-12-12T17:24:35.441270Z"
     }
    },
    "outputs": [
     {
      "data": {
       "text/html": [
-       "<h3>Version Information</h3><table><tr><th>Qiskit Software</th><th>Version</th></tr><tr><td>Qiskit</td><td>None</td></tr><tr><td>Terra</td><td>0.9.0</td></tr><tr><td>Aer</td><td>0.3.0</td></tr><tr><td>Ignis</td><td>0.2.0</td></tr><tr><td>Aqua</td><td>0.5.6</td></tr><tr><td>IBM Q Provider</td><td>0.3.2rc1</td></tr><tr><th>System information</th></tr><tr><td>Python</td><td>3.7.4 (default, Aug 13 2019, 15:17:50) \n",
-       "[Clang 4.0.1 (tags/RELEASE_401/final)]</td></tr><tr><td>OS</td><td>Darwin</td></tr><tr><td>CPUs</td><td>4</td></tr><tr><td>Memory (Gb)</td><td>16.0</td></tr><tr><td colspan='2'>Wed Aug 21 21:50:30 2019 EDT</td></tr></table>"
+       "<h3>Version Information</h3><table><tr><th>Qiskit Software</th><th>Version</th></tr><tr><td>Qiskit</td><td>0.14.0</td></tr><tr><td>Terra</td><td>0.11.0</td></tr><tr><td>Aer</td><td>0.3.4</td></tr><tr><td>Ignis</td><td>0.2.0</td></tr><tr><td>Aqua</td><td>0.6.1</td></tr><tr><td>IBM Q Provider</td><td>0.4.4</td></tr><tr><th>System information</th></tr><tr><td>Python</td><td>3.7.5 (default, Oct 25 2019, 10:52:18) \n",
+       "[Clang 4.0.1 (tags/RELEASE_401/final)]</td></tr><tr><td>OS</td><td>Darwin</td></tr><tr><td>CPUs</td><td>4</td></tr><tr><td>Memory (Gb)</td><td>16.0</td></tr><tr><td colspan='2'>Thu Dec 12 12:24:35 2019 EST</td></tr></table>"
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -453,7 +505,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.4"
+   "version": "3.7.5"
   },
   "varInspector": {
    "cols": {
diff --git a/qiskit/advanced/aqua/finance/data_providers/.ipynb_checkpoints/time_series-checkpoint.ipynb b/qiskit/advanced/aqua/finance/data_providers/.ipynb_checkpoints/time_series-checkpoint.ipynb
index 9bcccb871..647f93593 100644
--- a/qiskit/advanced/aqua/finance/data_providers/.ipynb_checkpoints/time_series-checkpoint.ipynb
+++ b/qiskit/advanced/aqua/finance/data_providers/.ipynb_checkpoints/time_series-checkpoint.ipynb
@@ -1,26 +1,22 @@
 {
  "cells": [
   {
+   "attachments": {
+    "qiskit_header.png": {
+     "image/png": 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"
+    }
+   },
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<img src=\"../../../../../images/qiskit_header.png\" alt=\"Note: In order for images to show up in this jupyter notebook you need to select File => Trusted Notebook\" align=\"middle\">"
+    "![qiskit_header.png](attachment:qiskit_header.png)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# _*Qiskit Finance: Loading and Processing Stock-Market Time-Series Data*_\n",
-    "\n",
-    "The latest version of this notebook is available on https://github.com/qiskit/qiskit-tutorial.\n",
-    "\n",
-    "***\n",
-    "### Contributors\n",
-    "Jakub Marecek<sup>[1]</sup>\n",
-    "\n",
-    "### Affiliation\n",
-    "- <sup>[1]</sup>IBMQ"
+    "# Qiskit Finance: Loading and Processing Stock-Market Time-Series Data"
    ]
   },
   {
@@ -36,6 +32,10 @@
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:26:04.194268Z",
+     "start_time": "2019-12-12T17:26:01.185784Z"
+    },
     "scrolled": true
    },
    "outputs": [],
@@ -53,7 +53,12 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:26:04.222806Z",
+     "start_time": "2019-12-12T17:26:04.211512Z"
+    }
+   },
    "outputs": [],
    "source": [
     "data = RandomDataProvider(tickers=[\"TICKER1\", \"TICKER2\"],\n",
@@ -73,7 +78,12 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:26:04.680768Z",
+     "start_time": "2019-12-12T17:26:04.238172Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -88,7 +98,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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9df+abvsa4Jwx1jKsudya0H+8NwCn7v5IfgFbtLdc1J5vQzkb+Gr1bAQOTrJiNNXtvTkc116ZlFB5RVVtB+geXz5g3tIkm5JsTLJQg2emWxMOHzSnqnYBTwKHjqS6vTeX4wJ4X/cW4YYkR8ywfxLN9dgn0duS3JPkpiRvmMuCUd77M6sktwKHzbDr0nm8zJFVNZXkGGBDks1V9VCbCpuZy60Jc759YQGZS83fAa6tqp1JPkjvbOzd+7yyfW8S/73mYtDtNbNaMKFSVacN2pfkp0lWVNX27rTy0QGvMdU9bktyO3Aivff5C8lcbk3YPeeRJPsDL2UfnKY2tsfjqqrH+55+EbhyBHWNwqK83aSqnurbvjHJ55Isr6pZb6CclLc/64G13fZa4NvTJyRZlmRJt70cOBm4f2QVzt1cbk3oP95zgQ3VXTlbwPZ4XNOuM5wF/HiE9e1L64EPdJ8CvRV4cvfb9Uk2y+01sxv3Feg5XqU+FLgN2NI9HtKNrwS+1G2/HdhM71OHzcAF4657luM5E3iQ3lnUpd3Y5cBZ3fZS4BvAVuAu4Jhx19zouP4MuK/7N/ou8Lpx1zzH47oW2A78kt5ZyQXAB4EPdvtD74+NPdT9t7dy3DU3Oq6L+/69NgJvn8vr+jV9SU1NytsfSRPCUJHUlKEiqSlDRVJThoqkpgwVSU0ZKpKa+j+geb/Hyy6QxQAAAABJRU5ErkJggg==\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -109,7 +119,7 @@
     },
     {
      "data": {
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\n",
+      "image/png": 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RCr1L96/plq8BzhljLcOaz60J/ft7A3DKnp/kF7Ele8tF7f02lLOBL1TPJuDQJKtHU92+m8d+7ZNJCZWXVtUjAN3zSwaMW55kc5JNSRZr8Mx2a8IRg8ZU1W7gCeCwkVS37+azXwDv7L4i3JDk5bNsn0Tz3fdJ9MYkdye5Kcnx85kwynt/5pTkVuDwWTZduoC3ObKqppMcA9yeZGtVPdimwmbmc2vCvG9fWETmU/M3gGuraleS99I7Gnvbfq9s/5vEf6/5GHR7zZwWTahU1amDtiX5UZLVVfVId1j56ID3mO6edyS5AziB3vf8xWQ+tybsGfNwkgOBF7MfDlMb2+t+VdVjfS//BrhyBHWNwpK83aSqnuxbvjHJp5Ksqqo5b6CclK8/G4EN3fIG4OszByRZkWRZt7wKOAm4b2QVzt98bk3o399zgdurO3O2iO11v2acZzgL+MEI69ufNgLv6X4FegPwxJ6v65Nsjttr5jbuM9DzPEt9GHAbsK17XtmtXwt8tlt+E7CV3q8OW4ELxl33HPtzJvAAvaOoS7t1lwNndcvLga8A24HvAMeMu+ZG+/XnwL3dv9E3gd8cd83z3K9rgUeAn9M7KrkAeC/w3m576P2xsQe7//bWjrvmRvt1cd+/1ybgTfN5Xy/Tl9TUpHz9kTQhDBVJTRkqkpoyVCQ1ZahIaspQkdSUoSKpqf8H3PDAB1T4R90AAAAASUVORK5CYII=\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -148,7 +158,12 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:26:04.878287Z",
+     "start_time": "2019-12-12T17:26:04.714254Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -159,7 +174,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -203,11 +218,16 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:26:05.118142Z",
+     "start_time": "2019-12-12T17:26:04.896507Z"
+    }
+   },
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -252,7 +272,12 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:26:06.148218Z",
+     "start_time": "2019-12-12T17:26:05.135481Z"
+    }
+   },
    "outputs": [],
    "source": [
     "stocks = [\"REPLACEME1\", \"REPLACEME2\"]\n",
@@ -275,7 +300,12 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:26:06.155018Z",
+     "start_time": "2019-12-12T17:26:04.351Z"
+    }
+   },
    "outputs": [],
    "source": [
     "if wiki._n <= 1: \n",
@@ -304,7 +334,12 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:26:06.162549Z",
+     "start_time": "2019-12-12T17:26:05.641Z"
+    }
+   },
    "outputs": [],
    "source": [
     "print(\"The underlying evolution of stock prices:\")\n",
@@ -401,19 +436,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 7,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2019-08-22T01:53:39.420765Z",
-     "start_time": "2019-08-22T01:53:39.409542Z"
+     "end_time": "2019-12-12T17:26:32.041399Z",
+     "start_time": "2019-12-12T17:26:31.944136Z"
     }
    },
    "outputs": [
     {
      "data": {
       "text/html": [
-       "<h3>Version Information</h3><table><tr><th>Qiskit Software</th><th>Version</th></tr><tr><td>Qiskit</td><td>None</td></tr><tr><td>Terra</td><td>0.9.0</td></tr><tr><td>Aer</td><td>0.3.0</td></tr><tr><td>Ignis</td><td>0.2.0</td></tr><tr><td>Aqua</td><td>0.5.6</td></tr><tr><td>IBM Q Provider</td><td>0.3.2rc1</td></tr><tr><th>System information</th></tr><tr><td>Python</td><td>3.7.4 (default, Aug 13 2019, 15:17:50) \n",
-       "[Clang 4.0.1 (tags/RELEASE_401/final)]</td></tr><tr><td>OS</td><td>Darwin</td></tr><tr><td>CPUs</td><td>4</td></tr><tr><td>Memory (Gb)</td><td>16.0</td></tr><tr><td colspan='2'>Wed Aug 21 21:53:39 2019 EDT</td></tr></table>"
+       "<h3>Version Information</h3><table><tr><th>Qiskit Software</th><th>Version</th></tr><tr><td>Qiskit</td><td>0.14.0</td></tr><tr><td>Terra</td><td>0.11.0</td></tr><tr><td>Aer</td><td>0.3.4</td></tr><tr><td>Ignis</td><td>0.2.0</td></tr><tr><td>Aqua</td><td>0.6.1</td></tr><tr><td>IBM Q Provider</td><td>0.4.4</td></tr><tr><th>System information</th></tr><tr><td>Python</td><td>3.7.5 (default, Oct 25 2019, 10:52:18) \n",
+       "[Clang 4.0.1 (tags/RELEASE_401/final)]</td></tr><tr><td>OS</td><td>Darwin</td></tr><tr><td>CPUs</td><td>4</td></tr><tr><td>Memory (Gb)</td><td>16.0</td></tr><tr><td colspan='2'>Thu Dec 12 12:26:32 2019 EST</td></tr></table>"
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -465,7 +500,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.4"
+   "version": "3.7.5"
   },
   "varInspector": {
    "cols": {
diff --git a/qiskit/advanced/aqua/finance/data_providers/time_series.ipynb b/qiskit/advanced/aqua/finance/data_providers/time_series.ipynb
index 9bcccb871..647f93593 100644
--- a/qiskit/advanced/aqua/finance/data_providers/time_series.ipynb
+++ b/qiskit/advanced/aqua/finance/data_providers/time_series.ipynb
@@ -1,26 +1,22 @@
 {
  "cells": [
   {
+   "attachments": {
+    "qiskit_header.png": {
+     "image/png": 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"
+    }
+   },
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<img src=\"../../../../../images/qiskit_header.png\" alt=\"Note: In order for images to show up in this jupyter notebook you need to select File => Trusted Notebook\" align=\"middle\">"
+    "![qiskit_header.png](attachment:qiskit_header.png)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# _*Qiskit Finance: Loading and Processing Stock-Market Time-Series Data*_\n",
-    "\n",
-    "The latest version of this notebook is available on https://github.com/qiskit/qiskit-tutorial.\n",
-    "\n",
-    "***\n",
-    "### Contributors\n",
-    "Jakub Marecek<sup>[1]</sup>\n",
-    "\n",
-    "### Affiliation\n",
-    "- <sup>[1]</sup>IBMQ"
+    "# Qiskit Finance: Loading and Processing Stock-Market Time-Series Data"
    ]
   },
   {
@@ -36,6 +32,10 @@
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:26:04.194268Z",
+     "start_time": "2019-12-12T17:26:01.185784Z"
+    },
     "scrolled": true
    },
    "outputs": [],
@@ -53,7 +53,12 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:26:04.222806Z",
+     "start_time": "2019-12-12T17:26:04.211512Z"
+    }
+   },
    "outputs": [],
    "source": [
     "data = RandomDataProvider(tickers=[\"TICKER1\", \"TICKER2\"],\n",
@@ -73,7 +78,12 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:26:04.680768Z",
+     "start_time": "2019-12-12T17:26:04.238172Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -88,7 +98,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -109,7 +119,7 @@
     },
     {
      "data": {
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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -148,7 +158,12 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:26:04.878287Z",
+     "start_time": "2019-12-12T17:26:04.714254Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -159,7 +174,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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888THxwPw6KOPUlRUxOnuuusu7r77bg4cOMBFF13ELbfcQmxsLMuWLePJJ5/kzjvvHLFtVICLyHnr5z//OZdeemlPeANcffXVAGzevBmAH/3oRzz//PP89re/JTExcdCvPX/+fKZMmUJdXR3Z2dksXrx4RGsHBbiIjIG/fnYv75U1jOhrLpk+le+tXzqs19izZw8XXXTRgPdv2bKFffv2sX379p699m6f//znewL9uuuu44c//OEp9+/YsYP58+eTnd3vOY4jQgEuIjKAefPmUVdXx4svvtjTzt1toCaUBx98kJ/85CccOnSIjRs3jmp9CnARGXXD3VMeLUuXLuWVV14Z8P6cnBweffRR1q5dS0ZGRk/zyofpbgN/8skn+cIXvkBJSQkJCQkjWXYPjUIRkfPW5z73OV5//XV+85vf9KzbuHEju3fv7llesGABTz75JH/4h3/Y0yk6GJ/+9KcpKipiw4YNZ3/wECnAReS8lZiYyHPPPcc//uM/Mn/+fJYsWcK///u/n9FuffHFF/PII49w8803U1JSAvg28O5hhNdee22/r//d736XBx54gHA4zFNPPUVBQQFvvPEGN954I+vWrRt2/ebc2F1fqqioyI3U+EcRmdiKi4tHZeTFZNff52Zm2/v8LYYe2gMXEQkoBbiISEApwEVEAkoBLiISUApwEZGAUoCLiASUAlxEJqWJdjnZP//zP2fRokWsWLGCT33qU9TX1w97GxXgIjIpdV9OdufOnXzpS1/irrvu6lmOivLR13052S9/+cscPHiQ4uJivvzlLw94Odlf//rXp1xOtvv1nnjiiZ7Hdr/P008/zZ133klnZyfgL3i1Z88e3n33XRYsWMAPfvCDYW+jAlxEzlsDXU522bJlPcvdl5N99tlnh3w5WYDrr7+emBh/+alLLrmE0tLSYdevi1mJyOh74R6o2H32x52L3OXw8fuG9RLjdTnZn/3sZ3z2s58dVu2gABcRGdBoXE723nvvJSYmhs9//vPDrk8BLiKjb5h7yqNlrC8nu2HDBp577jk2bdo0In/wWW3gInLeGsvLyW7cuJH777+fZ5555pRRMMOhABeR89ZYXk72q1/9Ko2NjVx33XWsWrWKL33pS8Ouf9CXkzWzaGAbcNw5d5OZzQZ+CaQDO4D/4Zzr+LDX0OVkRc4fupzs0IzW5WS/DhT3Wb4feNA5Nx+oA24fQq0iIjJEgwpwMysAbgR+Glk24Bqge/T6BuCTo1GgiIj0b7B74D8G/gIIR5YzgHrnXFdkuRTIH+HaRETkQ5w1wM3sJuCEc25739X9PLTfxnQzu8PMtpnZttNPTxWRyW0s/2TjZHCun9dg9sAvB242s8P4Tstr8HvkaWbWPY68ACgboKCHnHNFzrmirKyscypORIIrISGBmpoahfggOeeoqanpGTM+GGc9kcc5923g2wBmdhVwt3Pu82b2OHALPtRvA54eStEiMjkVFBRQWlp6xoWhZGAJCQkUFBQM+vHDORPzW8AvzexvgXeAh4fxWiIyycTGxjJ79uzxLmNSO6cAd85tBjZH5g8Bq0e+JBERGQydiSkiElAKcBGRgFKAi4gElAJcRCSgFOAiIgGlABcRCSgFuIhIQCnARUQCSgEuIhJQCnARkYBSgIuIBJQCXEQkoBTgIiIBpQAXEQkoBbiISEApwEVEAkoBLiISUApwEZGAUoCLiASUAlxEJKAU4CIiAaUAFxEJKAW4iEhAKcBFRALqrAFuZglm9raZ7TKzvWb215H1s83sLTM7YGb/aWZxo1+uiIh0G8weeDtwjXNuJbAK+JiZXQLcDzzonJsP1AG3j16ZIiJyurMGuPOaIouxkZsDrgGeiKzfAHxyVCoUEZF+DaoN3MyizWwncAJ4CSgB6p1zXZGHlAL5o1OiiIj0Z1AB7pwLOedWAQXAamBxfw/r77lmdoeZbTOzbVVVVUOvVERETnFOo1Ccc/XAZuASIM3MYiJ3FQBlAzznIedckXOuKCsrazi1iohIH4MZhZJlZmmR+UTgWqAYeBm4JfKw24CnR6tIERE5U8zZH0IesMHMovGB/5hz7jkzew/4pZn9LfAO8PAo1ikiIqc5a4A7594FLuhn/SF8e7iIiIwDnYkpIhJQCnARkYBSgIuIBJQCXEQkoBTgIiIBpQAXEQkoBbiISEApwEVEAkoBLiISUApwEZGAUoCLiASUAlxEJKAU4CIiAaUAFxEJKAW4iEhAKcBFRAJKAS4iElAKcBGRgFKAi4gElAJcRCSgFOAiIgGlABcRCSgFuIhIQCnARUQC6qwBbmYzzOxlMys2s71m9vXI+nQze8nMDkSm00a/XBER6TaYPfAu4JvOucXAJcBXzGwJcA+wyTk3H9gUWRYRkTFy1gB3zpU753ZE5huBYiAf+ASwIfKwDcAnR6tIERE50zm1gZtZIXAB8BaQ45wrBx/yQPYAz7nDzLaZ2baqqqohFVlc3sDBE02Ewm5IzxcRmYxiBvtAM0sGfgX8mXOuwcwG9Tzn3EPAQwBFRUVDSuAfvPA+/7W/ioTYKBbmpLBk+lQW5/nbotwUUhJih/KyIiKBNqgAN7NYfHg/6px7MrK60szynHPlZpYHnBitIv/yhsWsX5FHcXkjxeUNvLCngl+8fazn/hnpiSzOndoT7AtzUpiRPoXoqMH9yIiIBNFZA9z8rvbDQLFz7oE+dz0D3AbcF5k+PSoVAgtzU1iYm9Kz7JyjoqGN4vIGissbea+8geLyBl4qrsRF9vHjoqOYnZnEvOxk5mYnMzcrMp+VTEJs9GiVKiIyZsy5D2/VMLM1wKvAbiAcWf0dfDv4Y8BM4CjwGedc7Ye9VlFRkdu2bdtwax5QS0cX+yoaOXCiiZITTRw80URJVRNHa1vobj43g/y0ROZlJzMvK5mPzMngivmZCvURVNvcwQt7yllZkMay/NTxLkck8Mxsu3Ou6Iz1ZwvwkTTaAT6Qts4Qh2uaKTnRzMETTRys8uF+qKqJ9q4wSXHRXL0om48ty+XqhdkkxQ+6a6BHfUsHbx6q5fWSag5UNvHHa2Zz3ZKcUdiaietAZSM/2/IBT+44TntXmCiD2y4r5JvXLyR5CJ+piHjndYAPpKMrzBuHati4p4IX91ZQ09xBfEwUH12QxceW5nLt4hxSp/TfQdrS0cXWw3W8XlLN6wdr2FN2EucgMTaa9KQ4jte3cmtRAd9dv3RSh5dzjlf2V/Hwax/w6oFq4mOi+PSF+dxaNINf7Sjl0beOkpOSwPdvXsK6pbkMtvNbRHopwM8iFHZsPVzLxj0V/HZvBeUn24iJMi6dm8HHl+VxzaJsjtW1sOVgNa+X1PDO0To6Q47YaOOCGdO4bF4Gl8/LZGVBGgA//t1+/vWVEvKnJfLArau4uDB9yLUVlzfwoxf3sfVwHcvyp3LRrHQumjWNC2amMXWcRuC0doR48p1SHtlymIMnmshOiecLl87icx+ZRXpSXM/jdhyt4y+f2kNxeQNrF2Xz/ZuXMiN9yrjULMHW0RXmnaN1vHawmtcOVhNtxvKCVFYWpLG8IJXZGUlETdKBCwrwcxAOO3aV1rNxbwUb91RwpKal5z4zWDp9KpfPzeSyeZlcXDiNKXH972FvO1zLNx7bxbG6Fu746By+cd0C4mMG39Z+uLqZB3+3n2d2lZEcH8N1S3J4v7yR9ysaCDtfy8KcFIoKp3HRrGkUzUqnYFrigHu57V0hqhrbqWps50TkVtXYTjjsSJsSy7QpcaQnxfXMT5sSR0pCzClfioqTbfzHm4f5+VtHqWvpZFn+VG5fM5sbl08nLqb/0wq6QmEe2XKYB17aD8DXr53P7WtmExutS/HIwJxzlFQ18+qBKl47UM0bh2po6QgRHWWsLEglyow9ZSdp6/RdcykJMSzPT+0J9RUFqeSnDfx9CBIF+BA55ygub+S1g1XMTJ/CJXMySJsSd/YnRjS1d3Hvb97jF28fY1FuCj/+76tYlDv1Q59TcbKNf/j9AR7beozY6Ci+eHkhd350bk9zTlN7FzuP1rPtSC3bj9TxztF6mtq7AMhOieeiWdPIT0ukuqk3pE80tnOytfOM9zIDAwY6Ryo6ykhLjCVtSiwpCbHsOX6SkHNcvySH29fM4eLCaYP+ghyvb+V7T+/ld8WVLMpN4d5PLeOiWUM/MhkpbZ0hGto6yU5JGO9Sznu1zR1+D/tAFa8eqKb8ZBsAhRlTWDM/kzXzsrh0bgapif670BUKc7CqiXePnWRXaT27j5+kuLyBzpD/D52RFMfyglRyUhJIiI0iIS6ahJhoEuOiSYiJ8tPY3tuUuGgW502dcM2eCvBxtqm4km/96l0aWru4e90Cbl8z54xx6nXNHfzLKyVseP0wYef43OqZfOWaeWcNllDYsa+ike1H69h+uJZtR+qobmonKyWe7JQEspLjyZ4aT3ZKfO+6FL+cnhRHlBmNbV3UtXRQ19JBfUsntc29833XL8qdyh9dVsjMjKE3g7y4t4LvP7OXspNt/MHqmXzrYwvP6UdxJHR0hdlysJpn3y3jpb2VNLZ3sTw/lRuW53Hj8rxhbZ98OOccVU3tHKpq5oPqZg5VNXGoqplD1c0crmnGOZiaENMT2FfMzzynZrf2rhD7KhrZVXqSd4/5UK9r6aCtM0xrZ4iOrvCHPj8myigqnMaVC7K5ckEWi/NSxn0vXgE+AdQ0tfPtJ3fz4nuVrJ6dzo8+s5IZ6VNoau/i4Vc/4CevHqK5o4tPXZDPXdcumNRtxc3tXTz40n4eef0wqYmxXDgzjayUBLJT/I+N/9FJ6PnRGYnmlq5QmDcP1fLsrjI27q3gZGsnUxNiWLc0l8LMJF7cW8Gu0pOAbybrDvPCzKRhv/f5qrSuhZ3H6n1AVzVFAruZxsgRI0B8jD9nY3ZmEkvypnLFgiyW56eO2ol44bCjrStEW2eYts4QrZ0h2iK3htYu3vqgllf2V1Fc3gD4o9orF2Rx5cIs1szLHPOdDVCATxjOOZ7YXspfP/seAJ8pKuDpnWXUNnewbmkO37x+IQtyUs7yKpPH3rKT/MOmAxypaaG6qZ2a5g76+y+ZnhTXE+bTUxPJS0tgeloi01MTmR6Z728sfzjSOf3su2W8sNuPNEqKi+a6JTmsXzmdK+ZnndJ2X1rXwgu7K/jN7nJ2HqsHYEneVG5ckcfHl+UyJyt51D6LyaCxrZM3Smp47WA1rx6o5oPq5p778tMSmZPlg3pOZhJzspKZnZlEflrihOx8rGxo45X9Vbyyv4pX91fR0NZFlMGqGWlcuSCbNfMzmTEtkWlJcaPen6MAn2CO1bbwzcd38fYHtVwxP5O7r1/Iyhlp413WuOsMhalp6uBEY1tvZ2tDe89yZWM75fWtnGhsP+O56Ulx5KV2B3sCDvjt3goqG9pJiI1i7aIc1q/M46qF2YM6cet4fSsv7C7n+d3l7Djqw3xRbgqXzMlgakIMSfExJCfEkBwfQ1KcX06JrE+KjyY5PobE2OhxP/weTV2hMLtKT/Z0NL5zrJ5Q2JEYG80lc9K5Yn4Wq2enMzcrmcS44J4s172d3YH+bmn9KTsaUxNiSE+KO+0WT3pSbM909eyMIbetK8AnoFDYUVrXwqwMHaKfq46uMJUNbRyvb6WsvpXyk33m6/18R1eYKxdmcdOKPK5dnDOkE7S6ldW38sKeCp7fXc6+ikaaO7r6PVI4XXZKPJfPy4zcMshLTRxyDeeqvStEeX3biF8XqOJkGy8VV/LagSpeL6mhsa0LM1ien8oVkXbrC2elndOIq6CpaWpn6+E6qpraqW3yfUQ1zR3UNrdT29wZmXb0dKYC/O4bVzIve2hHcApwOe+Ewm5U21FbO0M0tXf5W1sXzd3z7X6+sb2L98oaeL2khtrmDgDmZCWxJhLol8zpHU0x3FpK61p5v6KBfRWNvF/ZyL6KRj6obiYUdmQmx7FuaS43Ls9j9ex0YoZwuF/T1M4Leyp4dlcZbx+uxTnfJHLF/EzWzM/k8rmZTEsa+7bhicw5R1N7F7XNPtyXTp865B81BbjIOAmHHe9XNLLlYDVbSqp561AtrZ0hogyWF6SxZl4Gl8/NJCc1gXDYEXKOrpAj7ByhsJ92hfz6cNg3M31Q3cy+ikb2Vc7sNjsAAAVvSURBVDayv7KRlo5Qz/vNSE9kYY6/1PL0tES2lFTz++ITtHaGyEiKY90yH+YfOUuYN7R18uLeSp7ZVcaWg9WEwo45WUncvHI6N63IY25W8qRuHppIFOAiE0T3GYVbSmrYcrCanZF243OVnhTHwhx/pc5FkSt2LshJ6bepqLUjxOZ9J/jN7nJ+//4JWjpCpCfFsW5pDjcsz+OSORnERkfR2hFi0/uVPLOzjM37qugIhSmYlsj6ldNZv2L6hBhSdz5SgItMUI1tnWw7XEdDWydRZsREGVFRRrQZ0VG9t6g+yzPSE8lKjh9SmLZ1hti8r4rnd5ezqbiS5o4Q06bEsqIgja2Ha2npCJGdEs+NK/K4eeV0Vs1IU2iPMwW4iJyhrTPEK/t9mO86Vs9l8zJZv2I6q2en6w+iTCADBfjEOl9URMZUQmw065bmsm5p7niXIkOgqwmJiASUAlxEJKAU4CIiAaUAFxEJKAW4iEhAKcBFRAJKAS4iElAKcBGRgBrTMzHNrAo4MsSnZwLVI1jORDFZtwsm77Zpu4In6Ns2yzmXdfrKMQ3w4TCzbf2dShp0k3W7YPJum7YreCbrtqkJRUQkoBTgIiIBFaQAf2i8Cxglk3W7YPJum7YreCbltgWmDVxERE4VpD1wERHpQwEuIhJQCnARkYCasH+Rx8wWAZ8A8gEHlAHPOOeKx7WwEWRma4DVwB7n3IvjXY+IBMuE3AM3s28BvwQMeBvYGpn/hZndM561DYeZvd1n/k+AfwJSgO8FebskeMws1czuM7P3zawmciuOrEsb7/qGw8w+1mc+1cweNrN3zeznZpYznrWNtAk5CsXM9gNLnXOdp62PA/Y65+aPT2XDY2bvOOcuiMxvBW5wzlWZWRLwpnNu+fhWOHRmlgp8G/gk0H3K7wngaeA+51z9eNU2HGb2Mefcxsh8KvAAcDGwB7jLOVc5nvUNlZn9Fvg9sME5VxFZlwvcBlzrnLtuPOsbDjPb4Zy7MDL/U6AC+AnwaeBK59wnx7O+kTQh98CBMDC9n/V5kfuCKsrMpplZBv7HswrAOdcMdI1vacP2GFAHXOWcy3DOZQBXR9Y9Pq6VDc/f9Zn/EVAOrMcfFf7buFQ0Mgqdc/d3hzeAc67COXc/MHMc6xppRc65v3LOHXHOPQgUjndBI2mitoH/GbDJzA4AxyLrZgLzgK+OW1XDlwpsxzcHOTPLdc5VmFlyZF2QFUa+/D0i4XC/mf3xONU00oqcc6si8w+a2W3jWs3wHDGzv8DvgVcCRJoX/oje71xQZZvZN/DfqalmZq63qWGi7rQOyYQMcOfcRjNbgO/gy8f/Q5QCW51zoXEtbhicc4UD3BUGPjWGpYyGyRoIkzUMPgvcA7wS+XdyQCXwDHDreBY2An6C71sC2IC/EmFVpIlo57hVNQomZBu4BI+ZTcMHwieA7Mjq7kC4zzlXN161DYeZfe+0Vf8c6bfIBf63c+4L41HXSIiM9CrA97809Vnf0+4fVJFtywfemmzb1pcCXEadmX3ROffIeNcx0oK8XWb2NeArQDGwCvi6c+7pyH09nYBBZGZ/im9qnXTbdjoFuIw6MzvqnJtMHWNAsLfLzHYDlzrnmsysEHgC+A/n3N/3HS0VRJN52043IdvAJXjM7N2B7gICO/Z2sm4XEN3dtOCcO2xmVwFPmNksgt+hPpm37RQKcBkpOcA6/LDBvgx4fezLGTGTdbsqzGyVc24nQGRv9SbgZ0Bgz0eImMzbdgoFuIyU54Dk7i9NX2a2eezLGTGTdbu+wGnnHjjnuoAvmFmQx7fD5N62U6gNXEQkoII8jlVE5LymABcRCSgFuIhIQCnARUQCSgEuIhJQ/x9qmkzIdIqr1AAAAABJRU5ErkJggg==\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -203,11 +218,16 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:26:05.118142Z",
+     "start_time": "2019-12-12T17:26:04.896507Z"
+    }
+   },
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -252,7 +272,12 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:26:06.148218Z",
+     "start_time": "2019-12-12T17:26:05.135481Z"
+    }
+   },
    "outputs": [],
    "source": [
     "stocks = [\"REPLACEME1\", \"REPLACEME2\"]\n",
@@ -275,7 +300,12 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:26:06.155018Z",
+     "start_time": "2019-12-12T17:26:04.351Z"
+    }
+   },
    "outputs": [],
    "source": [
     "if wiki._n <= 1: \n",
@@ -304,7 +334,12 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:26:06.162549Z",
+     "start_time": "2019-12-12T17:26:05.641Z"
+    }
+   },
    "outputs": [],
    "source": [
     "print(\"The underlying evolution of stock prices:\")\n",
@@ -401,19 +436,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 7,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2019-08-22T01:53:39.420765Z",
-     "start_time": "2019-08-22T01:53:39.409542Z"
+     "end_time": "2019-12-12T17:26:32.041399Z",
+     "start_time": "2019-12-12T17:26:31.944136Z"
     }
    },
    "outputs": [
     {
      "data": {
       "text/html": [
-       "<h3>Version Information</h3><table><tr><th>Qiskit Software</th><th>Version</th></tr><tr><td>Qiskit</td><td>None</td></tr><tr><td>Terra</td><td>0.9.0</td></tr><tr><td>Aer</td><td>0.3.0</td></tr><tr><td>Ignis</td><td>0.2.0</td></tr><tr><td>Aqua</td><td>0.5.6</td></tr><tr><td>IBM Q Provider</td><td>0.3.2rc1</td></tr><tr><th>System information</th></tr><tr><td>Python</td><td>3.7.4 (default, Aug 13 2019, 15:17:50) \n",
-       "[Clang 4.0.1 (tags/RELEASE_401/final)]</td></tr><tr><td>OS</td><td>Darwin</td></tr><tr><td>CPUs</td><td>4</td></tr><tr><td>Memory (Gb)</td><td>16.0</td></tr><tr><td colspan='2'>Wed Aug 21 21:53:39 2019 EDT</td></tr></table>"
+       "<h3>Version Information</h3><table><tr><th>Qiskit Software</th><th>Version</th></tr><tr><td>Qiskit</td><td>0.14.0</td></tr><tr><td>Terra</td><td>0.11.0</td></tr><tr><td>Aer</td><td>0.3.4</td></tr><tr><td>Ignis</td><td>0.2.0</td></tr><tr><td>Aqua</td><td>0.6.1</td></tr><tr><td>IBM Q Provider</td><td>0.4.4</td></tr><tr><th>System information</th></tr><tr><td>Python</td><td>3.7.5 (default, Oct 25 2019, 10:52:18) \n",
+       "[Clang 4.0.1 (tags/RELEASE_401/final)]</td></tr><tr><td>OS</td><td>Darwin</td></tr><tr><td>CPUs</td><td>4</td></tr><tr><td>Memory (Gb)</td><td>16.0</td></tr><tr><td colspan='2'>Thu Dec 12 12:26:32 2019 EST</td></tr></table>"
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -465,7 +500,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.4"
+   "version": "3.7.5"
   },
   "varInspector": {
    "cols": {
diff --git a/qiskit/advanced/aqua/finance/index.ipynb b/qiskit/advanced/aqua/finance/index.ipynb
index f4291effd..59ef6850f 100644
--- a/qiskit/advanced/aqua/finance/index.ipynb
+++ b/qiskit/advanced/aqua/finance/index.ipynb
@@ -1,26 +1,22 @@
 {
  "cells": [
   {
+   "attachments": {
+    "qiskit_header.png": {
+     "image/png": 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"
+    }
+   },
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<img src=\"../../../../images/qiskit_header.png\" alt=\"Note: In order for images to show up in this jupyter notebook you need to select File => Trusted Notebook\" align=\"middle\">"
+    "![qiskit_header.png](attachment:qiskit_header.png)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# _*Qiskit Finance: Overview*_ \n",
-    "\n",
-    "The latest version of this notebook is available on https://github.com/Qiskit/qiskit-iqx-tutorials.\n",
-    "\n",
-    "***\n",
-    "### Contributors\n",
-    "Stefan Woerner<sup>[1]</sup>, Daniel Egger<sup>[1]</sup>, Christa Zoufal<sup>[1]</sup>, Andrea Simonetto<sup>[1]</sup>, Jakub Marecek<sup>[1]</sup>, Martin Mevissen<sup>[1]</sup>, Shaohan Hu<sup>[1]</sup>, Stephen Wood<sup>[1]</sup>, Marco Pistoia<sup>[1]</sup>\n",
-    "\n",
-    "### Affliation\n",
-    "- <sup>[1]</sup>IBMQ"
+    "# Qiskit Finance: Overview"
    ]
   },
   {
@@ -74,7 +70,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.3"
+   "version": "3.7.5"
   },
   "varInspector": {
    "cols": {
diff --git a/qiskit/advanced/aqua/finance/machine_learning/.ipynb_checkpoints/qgan_option_pricing-checkpoint.ipynb b/qiskit/advanced/aqua/finance/machine_learning/.ipynb_checkpoints/qgan_option_pricing-checkpoint.ipynb
index c999e22e9..79ddd342f 100644
--- a/qiskit/advanced/aqua/finance/machine_learning/.ipynb_checkpoints/qgan_option_pricing-checkpoint.ipynb
+++ b/qiskit/advanced/aqua/finance/machine_learning/.ipynb_checkpoints/qgan_option_pricing-checkpoint.ipynb
@@ -1,26 +1,22 @@
 {
  "cells": [
   {
+   "attachments": {
+    "qiskit_header.png": {
+     "image/png": 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K7KdEZsu7QvYk/wuyZbahTeNE7Q/YTF8+gXY5+vSriPjkPZAYM/2yHpv9pM2pQ5svlCDWSeTlgxgzkXdQVLYP0OGiDNpS7mHR5m44iMn2bRZ5x+Xg0dmSbMPuI8d60RtCf8Fm1tofM8FFvh+YEM1nYkBKspxfaq3Y7HgsML9bkPMsFsT6C3kjQIzxyDsnJttkdKgOg3ZNNCHidR2IyXasGXnlavDobEm28iIWOYYSW8Fl2MJcvW8QBS5JOIuIL1aBdNR5ZTVmbLE/hgKYqUcO+54CRLqPvFQQIxt518Vk+zY6/AAdyLfgZT8Qk+3gJuTd8gPPzpZkqz5iL+JBOHD8t5kc4Vb9OFYDLvCe04RovZwEEgmd83utI1r9Fh1wAm6yyDmrBZFeSZLtYzHZLkGHUuhI4RkfEJPtgBrkPQoFz86WZEsl1iLH+ElLg4X8/clUe2nJABq6FPcbItbukIMEvIfuuNNkwVa3Z7SEusVoTySTAnEskmRbLyJbYXjDpTrdIuIp8qriwMOzJdkqStGmfjg0U/T72oCtrPrKX4tju0qFKahGxOvDoLs0mesv1hpZbFX3aT8FNMuoRpuNDIjijZJki7SIbA+gQ6RE2QZUIK8+FTw9W5Ktz2l7n5fU/PdMuog8s/7Jb+8mqaEzfb9GxIbO1nm97KAzutFbymsNVnQwnRingVbMSQtyrvqDKOESZesnIttf0WG0NNmqypFnGA6eni3JlkqpRY5hLfDogOK/UcBseHVh5+S+jHOIArLRTxGxYmLn1ToDIW3avLKKemGzaD47zY8G3vsGe8pDaRAjQaJso0Vk+xk6LJEm2/3Is0wCj8+WZKtYhDav0kBIETT7vAUFrMbGJye3FQzwea3BPnZeoTtt6Zf5dZVt89V2wH3LFi8dkvXe11erDSYWBeoP5vvLQah/JdqsYcS1J1G2qSKyXYMOP0qS7VIUmAMkW5Kt9xF7lReU4EyunfBzDTqxGJpe/v398vGxPv/5Tx8bPkQq6w4iPpwF7XOq3BH7fxQhg2fvPH6/1mC2opNHe4ZpZeCMPmpBznkNiDFcomwzRGQ7Hx0aEyXIdpwVne7fJFuSbUwlcgzroQ3aJ3X1X0Z0xpr1jVVXftsya2hfb8rLzl5iwCoroulYaFfZetlRqpCMgmXfnv2n1mC0sOis+khxtIoGAeEouS6FAhFSJcq2n4hshyDvH/9uZxtf99peSZItyTbfXk39MGgP4z9qx3UTvo61GAx1z68f+WL1rJyUcB/aNtZNvICIzxZBJ7jLvEMTRsxY9slvfz2u1evNFmyj+vfSFI0C2pNcgzbz5CBCjETZhorIFp4h71xMN7PVViDvayDZkmxb/5ewN7XQLkrGhI/bfKkO28GaTQZ9Y82jmxd//XLr0tKfWETLpTRfH0ZBQTOaYbx9A0P7xqWOnFT0wYZPDpy+8ehVg95gMrPYnsc/l2QEKGQUtEt9yYqc75UgQqBE2fqIyXYPClj2xXcnW+oo8o7SJFsOyZa+jBzTPmiLVqo1Go2PWqkKHvzuj3easAOs1WwyGgwm5BgfXf/70oVTx+1OnLl06a8bFQ+f19Q3NDUZjEazhcWOvDq3dXKcVqlS+2i0GrVS5gVt7DIi504QiKCQJlsriMk2A51Yz64fpXE32y0ocEVGsuWQbCNqkKOfC69hvAN0QUGBfhqtj1qtZOTqiGEL912vwy6xVqvV0srKYbEr5qendxQm+zMKRqX29tFo/AKCgoICNAwFziY3IccwmAIRDJJkWycqW/gOX2e9+tWHeTG06Gyno5PlJFsOyXacvaraJBDwUgWERUZGhAUHBQZw3Wq8vVUMw8hpRpcybdPhe3UoHePzP75amBOjkdMKhlHaotVqfAMCg4LDIiL7hgeqKBCIeoY24o6UqpQk2yfisg2qwXYZrn6xIIMRkW2qHp0Y+pFsSbYAq5pvBVq+WSYwOr5fbEwU160uKMBfq9VqfLzVKiVD0wAyRh2YMmn5vvNPGljsFv2La79tmzs8WssoZF5AyRS28bGtWj//wCBdWEREVGxs//gYnQocmAtW5HzCgAh3kZcNYoxC3i1x2cI0FjtkOv1usIvZ6h7ja/6gSLYkW/gNOeYfoQWliUpKSU4cOCA2NioiPITrNsDfV+utUshoaN3ZRMkYpXd4+qSSHT9fvFtjFJmvqfaf8oMfL56aHe2vZmQ0/wYikClUaq3WPyAgSBcabq92YEJyalIsv1fqEyNyLmpBhD+RNxHEmI68SyKzhXksdoK9UEi7kG15ObbxAcmWZKu6KzzTXB6WnDlkUHpaStJA7n7bNzwiXBekVStkjmIFKJmcUWlC4gZNmLNs+76jf9x6WCncUCxkNdS9eHznr9/3f7Rm3qTMfmFaNWMLludol5LJ1dogXTh3r42J7TcwISUtfVBmZkq4HOzm6pHzrC+IcBR5c0CMEuT9JDZbeNeKnaqYTnWRbfuaokm2Hp9tTANymsbZo43MzMkZMSxrSHpacuKAeO5+GxqgllPgXGx7+dpmpb5hcYkZ2bmTp89774OV6+3WLil5r3hG/ujsQUlxEf4+ahXDyPlc22cbL6v9Q6Ni4/rbqx2SNXRETm5mtAI4Q2qRY8miwXUbkbfY7cPjVorOFrLvY+fKw93JFk+TbD0+29EscirjAGR9c96aOG5U7sgRQ7luU1MGRgWqbLdZEIGiKa8+IJMpmBYKhVwmk9EUBWJ4Aa0K7DswOTU9Y0j2iJG5o8ZNnJwTLQMIe8gip0gOrstH3mYQYw/y8sRnC6rdJuxU1UgXs31exCJvHsnW07MtQQ573RvCxr1dWJD/1oRxuVy4wwYPCPGhwQvE8qLkcpWPigK38eV6B/fPGMZFmzs2b1J+QeHMcZFAN69JbWDAdVHI+wrE2I+8cBHZ8kI312BnLONdyrZ6IOxFXl04ydbDs92DHPMvqhHvvlNcNLNwSv7EvLwxWXEad8OjlT6+umA1SMM7asjovLwJ+QUzZs4ufufdXNWXJuTsV4EIgnJugBj/CMIBUdny1IV772DH6vu7kG1DBoDvC+QdIdl6eLZH7dmWTV9aurBkfnHR2zOmT84MlYP7VIFhUf3DFCARefCgidNnvF1UvKBkYenSwr1G5JR7gwin0IENcPMd7E+Jz5YXMHFbuQnbd1/ZZbaGkcCZhgKFJFuPzlZxFTmW31esXLZk8fvvLJg/MzMIukUWHJ80ZFAISMcvY/q8+e8uLF2ybOWKg2bkPAoGEbYi7y1wXSHydriXLU+VPX/3iccstlHaVbbmCW2fT1KtI9l6crb+j5Fj/nX9hrWrlixdWpIbBN1E+aUNHf3WEA1IyDe7ZOnS5avXbVi/39wy1xNhKvJ2g+v2Iu9t97MV8h5Ssq8anVR5d54tW9je08B+Itl6crYRjc2D5O3bNq/fuG5qGHSfKnnitOLiZBqkFFKwZtOmLdt3fG9EjjGTcnNN6rp7ByyzkdJkayMbdRaFFnSebQm0WoIC+SRbD842iUWOoezT3bs+WplGgQTo8JnvLds0LwKklbzs448++eyHRrQZR7v7XIIMt47FuAASZMvLb0TewU6zXQEO8uvIq/Qj2Xputjlo0/jL119/+24gSEPz9sadX32TrwBp+RV/8+2+n2rRplDu7m6nMrd2V82XNlsYjrx6eSfZbgOBwVbk7SPZem62+WhTf/jnsqlykAg9as+Bo5e/HQQSoyft/+VwDdosVIAIASZ0sEaDawaw6GDylzhb+BZ5KR1newycfIwCY0m2HpvtbLSpO3V0KkgnduOl68+q1mpBahOPnmrOdjkDYvyKvI/ANV8h7yBInW1QnbA/V5+4p32OvCcakq2nZvse2jSULwAJyd6+2mDGCzkgudmXatFmKyMud+Tp48AViUbkTZE8WziBDjNdzhbyUeBzkq2nZrscbZq+VIKU+h2w9bHZD6Qm/6gRbbaJy1b+CnlXFNA15S3k1Sulz1bQZqnr2cIh5LHDSbYemu0ytKkZBpKiZt5HxL/zQHKpL9DmS6XoSHjboWufosA2kD7bYnSYLyLbiEbkPVCRbD04W+sVNUgr7HMzonlPMEiNOW9Fzrcis9XVIo8dJWpUjZUad7IdUa7renZiN0lEtrAIBXaSbD0z2+3IsRwGqb11DRFvTwHJlZndyRZKUKBuOHRuTCMKzAI3si0y4TVf6Ng+dBgsJlv6CvKsg0m2HpntOnu25yiQWOAOA6L16yiQ2jGLW9nSV1HAOAU6U2RGgXJwI9sNXb3Hw210iBKTLaRZkHebIdl6YrZL0eZFAkht1CVEfDSbBmnFPEObr5Ug0mAWBdj3oWMrUIjNEJ8tcwDtTjHQAa0VW9XIRGULu1BgA8nWc5ekTJ+B1DTrGhFxf3+Q1sdGtNnOgFhfopNjA6B9aWfQydcgOtvAS9jiRiK07wd02AfisvV5jDxzMsnWA7NdgnYNM0Bq2acR8XmJHKQ0vRHttonPNuAlOrHs1UFbkd+z6KTCT3S28Q/QwbiYgnaUIm+yyGxhAgpclbmT7TmSba/Odh42q8wDiSmX1iDi4XSQ0LhKbLaaAdHSG9BZw7cFGueyCw8Y0FllXxCbLV2BQmdioI08CzoYvMVmC7+gwAp3sv2CZNursy3EFlVTQWJphxCxZokSJFP0ClssUoB4Iwz4OtPJJTNyEoIUusTcwpUXrPi6hhQQnS0k1qAQe6pQCUK5Z1FgG4jONrQeecb+bmRbig5ly3lToFcg2eZhK/1aNUhK/n4lIp7MBolodxmx1Sw5uGGSBcUxjwI3soX0enRW9/nUrEg5AKjic+ddRqHnPuKzhRIUKKfEZzse23UMegWS7RB0sJzMAEkNKEPExnUakERuuRUd8mTgjtkozixwK1vIbsK22Mqbr7CNQnAjW6ocBRaJzzaWZNurs40zIa9mUyhIiJrzEBEv5YIE4j83IM+cTYNbSszoutrJ4Ga2kGtE15SBO9lCkhl5TTGis4U/Sba9OdvgKhRg7y0KAulEfW1FNGwPgO6KWP2CdQoqiQL3ZNxDV/3ZF9zOFka/RFcclLuXLWxFgTPis023kmx7cbbeFejEWvFhOEhm2h1EvDYRumfgtqdWdPI4HNyl3ouu2aWAbmQLuoPYtRMMuJmt6iEKzBedLXxOsu3F2cJ55FiuPcZW7LM9GTRII+QTM6L5szBwnzp33ysWW909bUbOVR9w38SX2LUXEwDcypY3ux47x+5WgrvZwlgUqA8Xna1/Jcm2F2f7A3JMXw44jjzjqbkRIIm8K/ZHy4GbqITlf5mQtz96txE5B1XQDbrttdi5+wuU0O1sIfJs579INoD72cJ+597EZgtBB0i2vTfbVcixnAPlgofIYyt/nBIM3ee/1YDI7osGN8jiSk7Uo8C1KTI4ZUHODga6RTX3GnasPJ8CkCBboAo7/mVerFdBt7LV1aLATJHZ2kx4RrLtrdlOsVf6UAcQuv6RkRWU+7xs3gAfRgbdMvwci/hkgRzEoBTK4BFrL9Sz6GA13FjkDxB4l0XOAgV0V3ZZI7bDenVPNvDczJaX83MDtuPMFAVA97KFeShQoxOfLWjyFn95sZpk2wuzTTMhp3aIrZb47Q8NVhR4eXptbpiKocFt3itqEPFgshfHxWQZbdrcb+6YUMBiuF4aApykantbOTR0H9VvxvYztcgznFk/SgNSU4zcfqYSeYY/dk2PBHcRJFt+aUI/G+xiV9/Sm1HI8vC3Zbl91YycgnZ5CUFbg46yiFWlSq/XQVsyRhmUPnfvX3UsCrCmpgslgWA3XY+cV3EgmfHo8Dn0GN+M0flFJXMmj0yNlgPRXSRb+hxyjLuhRdCsYzUGKzqxPP5964xUHdcuLYiV+6yPF6cPp+MUmUVViHgik7vOcWGfNvVSNKP07TduedmdJnRmMTzb/5YGWmw1IudvX5BMqjDb3oEg2cKn9jjOyPmQ0zdcaTSy+JrGO4e3zMyO0KpsN94+LcV6dd1t4k8sYu0aHz7bPvxroQ/QCqVPcMpby74rr2axTbOvTi/qJ4NW9DELcvYpSbaejWQ7C22eRICAduyntxqNVmzD8Kz8+7Uzh/ULUMplMrCV2GW2suLHiHguB/ibLfeK//TxomkZ4xc7ZMqyL049qLPi61iz4eXZ5RkMCAQ/spe9UEGy9Wwk20QDcprywFnA6J3ltQYzi+2wvrxz5uuVc8alxgR6q5QKhZymKQBhtEJx+6yI+i0BtqopSqZQMEq1Ljpl7OxVe4/feG7E9liM+keHPkhlwFluA3LMOTTJ1rORbL0fIMewCdpQp5X+9qDROV0h1vjy9sXDX20qnTVpZHq/0CA/jVqlUioZjqIVwzDKwgpEvDItKCw2dVhe0aJNXx2+eLtKz2IHrEb9y4t7Cvq20+YqA3IehYNnZ0uQbGF/84YLBbSDCh21+sj9JoPZip0yNdVUPrh26fTBn775YufmDcsXlTb7cO2Wzbt/eWyLsfJ+ZXWDkcXOsGajvrL805mJKmiP/IQFOYdUnp4tQbItQZsX/aEdXgC0f+KEVfuvVjYZTFYWe4zVYtRXVxzbNTc7xp+hwAvaEfUMbZYqQDopJFuiN2aboUeOfh60RfmERPYN1/lptCFp+at+KH9arzeYLdLGy3LBGhpf3jqya/bQ2OCAwLCo2Pi4MC0NbRXqkWPMpkE6gSRbojdm6/83csxl8Dp5YMzA/rHREeGhQUH+PoxCGRibW7y57OLD2kaD0WTp5r2XZS1cr031T/48uHvhxJQwrcrHLygkIjI6Nn5AQkpy/xAlBa/53oScm0EgpVfY6tJCgfFAEG9wtswOe0VPIkGIUoUnpackDIyLjYmMCNUFBfhrtSoF9PECJiRpwvvb9l+4/ayuUW/rV0zArNViMRkN+sa6Z7cv/LxncUF6hDcFXn1opdYvKCg0PCIqNm5AQnJa+uCsjBgfGoR091nkfMyAlC5gu34BgniDs6XHWpCjLwIBdezgYZkZ6SmJAwfExUaFhwUHaFVyCgRobUzGmKLlu/YdvnDjEbfa1NikNxi4ik1ms4VnNptNHINB36RvaqitfHLj/KF9O5cVjcmI8ZWDEC1TB4RExtqqTUrPGJI1dGTuiIHewnALmuzhT5SBlPaSbIlemC2EVCDH/AufiDxq5LjcnGFZQzLSkxMGxMWE+avl0CHGJyg6eei4KUULV2ze8cXXPx86cupcizPHDh0q+2bvrk0r3p8zNW9EalywDwMd8pKrdVEDElPSMzKzR+bmjs2bPLafGhx+MCPnQRhIaibJluiN2TIfo83zeGgmC86ZUjBxbG7uyKFZg9KT40PUNIhDyVvQjiL/83//BzLaC7oi84lIGJw9PCd37PhJBQUzZ02IlEOzqCcscvYqQVLUnyRbohdmS482IcewBOyY1KLimVMLJueNG507YnC/QAVIwItWqDUB/gy4QBGcOHzM+An5BdNnFs0rmT/Uuzn2EgNyLJNkIK00K8mW6H3ZQuA15FjLfWx9+U4sfX/+vKJCLtwJI/trQCKU0i8oNEonB5doE0blz+CiXfDeBx8unxpKA4DyrAU5t4NBap+QbIlemC2zFh37kkPmrl7y4aL35hXPmTkp0Rsk48XoouISEwPBRT4Jk4sXvL/ow6UrV298P4oGGFGHNlsYkBqzwUSyJXpdtlRKLXLMZTSEfLB1w9pVS5eULpybqQUpyYIGpAwZmaoAV2mGFH+4YvXajZu37l4RDdRX9rYahlAgvcRyki3R27IF1c9oU5WsWfzZ7u1bNm9Yt7wgDCTmmzRibH5BMLguZPLqzVt37P7k86/W+cU+RZsTPtATqGnbDlaYSLZEb8pWNsmMHOMnM3/86vPPPtmza02WHKQm7zehsPiDMWK6k/8/e/fh0NS993H8H3nuvf1mgmAAARdOcNGiRdy7tigWtO7Zwa3bqrUW9x5VXGC0roqrqFVcOCpOlkzDDPOQ9XnOyCEhhhDUXPTe8+o+g5P8ct4c8supDl63/7fEI0nHTy/c3gCWeaaSPEbRI8ymM0kkH3a21OEBONVXTp86eSLp6K99yAOCv1qyetOm/gpqhZ5rjp88dfrMuT8rwXnekQQSiZStKt4MliXn0uVLF85v6UqeoByyZteh86taV16nX1MupV65km0By7JKTRKJlK1Vl2xwmIe3b9082p08o+vikxfTH072otbovC/t9t2HdeAU9CaRRCJlq14jXM7KHj+6PZY8RBn9x5OC+vNh1CqRqRlPXguPbquaJBIp20bdcsExFeUekJOn9Nhb2oDauT7UKgnZhQ3g6PqRRCJla6NaZgKHKYklj5FNfQ4g7TNqldG59eCYN6pJIpGytRPyHLyqSPKcjjuNgGGlP7VG/zzwXoWSPYlEylb1gwGcuq/Ig8bnA3gcJaNWGKEDx/iTmiQSKdsmgu+C07BPSZ6jSWEAy4Zgcp/i12pwMrpSUxKJlK0irh6coijyoOnZALLHKMhtEZlmsJh5SpJIpGwd+J62gGW6HkKeozleB2B/CLmr83kGnCv+5EgikbKVRb4Gx/hHiJw8ZuIzC6CLVpNb5CFJ9eCUj5bTe7IrOfkr+oCMWXssOTmSWiMxOXkUuUO5IGm2jDhHkpOHk6OOycnJIcQZeXB1MP2PGfszO/Cff+zZknqtQej2wgA1eYrX/ioAx3uRO9SfWa+1xu1qegvB3ZroSpwCYAV9OJLAiaPWqAYWkjsmAxhNHAaYQ45CAXxGrHY1wF7635IMztcffbbU6RZ45owpGhl5yNBHFkA/w5taJNdMyzCBl9GN3kYamnj9AWY7E57MNgHAcjeyjQBwj/6nzMJ/S7byCZUQVO0IbUeeodhYDuBSP2qJz6f7qiGonqL46LJd+uDBTGrZVWDToEGD/DyTbRSAfm5kKysC1tBHoPuDB3/Qe/EXsNHlwD948EDxUWRL6s0NEJif/BDioXDD75iA+u/bkysyn57LMk0QGA+r6W2zNTM2r/6T2e5y7zD5wLdk856zpV5zu5Eb2ZJm+lD6GPQFcui9KGxpDAGoPo5sqWOaBVam9G+7tpORByx/DeBOBDVP0T50WYYRor8H0Ftne5XsfXjZ6j2arY3LbFvBb8a8eR3+G7Kt/i/KVj68CI1Md37sr1HRe9ct1QAYVwZQM9QBkeufG9GoLFb9EWa7978y26n1ACKpzYT/x7JVfETZkiq+DjamrB3jgn0U9J4tygfwZKharXDSrKbntGSdBTbMr+3Jo9mqyYl2dutlJHK5i6zJ0keOh/F2L1uFwv1TTi1762y9W8hWriZH3jKii3Carasfy5RKcpMPiZodt0VOs3X/9XM/2+Fvk626jbIln0QD7FXfWDU0yEdJ71PwOQawHIiLHhPWQdXkvNeETNr51Ah7xvOdyXPZDtyUWmF6emyRkqy0Wm0U9fy9EjU3pxKrd3KRpS59nZIaef87+aW59EpCH7KaqdUmkCr+hcGceTKUeLtP5gJ4zH6xL4mjWXu/HOw+46mJg1qtAbjPbmYtYcr+dKY+ff9kEiVqtWOI+i757egbp1yvAzpU/7W5Owm82a8SToJorXY3cXqxCwPfyNZ7VYoO+gsj7bLdp9VOJM4PWu0ydoMco/HZsS7UyPfXGzqY8hL11+Lj44PInmbJIz2YlxeiyYmIXXcZc/7mYJrBDRJnsla7k6z2iuNDFLYnh4H+0dFQsurMPvQQirqsR9k16yOfpL1pBGrYFTuItUOrFQeqP7vMl1gyfix7n65EzY04YoUeL7LU3lurIJE4rMbGge/J/kN8Sgu12lVENFB7uR7A7+waOREt02rjSeDHLgojznatdgpRj/i9WrWwZvmpHEtxys8hbZEthaSZ0YSl8uqqkZ00ahm9N3GZFsDUYKzLPTreX0Y8RbuAPrG7Mxg0ZcmIkHsu2xVGCO6Jgw1gWpdy8I6rKNoAXnpnsur/HIK6eSTYAdynA+DVziJOHkTLifWVHlaJarJTAVEcsdqfgFWyr93FWHnEDNQ7ZjtcD17ZCGs9ACaQYBWQT5zBALo5Ztv1b/Asi23ZvgaWEucEkEJ/gFfRWGKcDlZnyMGIUlilhZCj6Qx4+aHbgQfEWWN3vSwClhHvJzMExr3yxnexCI8ygnfah98Iomxi5dhmv8cC6CD+aBsbUglekpKmGMG705Hs6SH6migSQA8SHAFSiWgSGimJKAU4QYJOAIYTJwv4mXYYAfgQa0g+BJWT2iJbWVQuHFmq722fOiDwvaXre6IOVrq1ITJSevt3HfrjqTwT3lAYrSZPZau8woV2+2yWBaiMasx25h0w+Uaw1vVrgCm/DqzTJIitByzZZ+/UADhoy3YaUKYDyxj6ZrZhVYD5yVUd33/HZrPtkQtWQSFY2SG2bI8Db2a7VAe81oNlmNK6bHvrwGLyTbDMdJ7tYkBXBlZNEPE+NwLlSQl7ud5/dKjWCKAhPcsA4K6cmloGTkkZ8HCHy2w3g1V+txisNbZsR+WDyTOCtaEV2U5P558da024QXz9Tnok2x2ANdvvjID5xen0egC/tEG2pIgrgxPmwtSEaeEd/bxV795uYHINRExCaI+R3yWm6+GM/od25LFs4wHzFhURDS8AHjdmm/N4nIzUCxmg5nrJXDXJhr4CLAOFOMqAjHAi8kkCMEbMtrQyIZgo8Ih4OeoXEaEDdkdERAQR0SngVV8i6rnKiOs+ZBMeEVEDbGQ30xDReYBZpSHSrGGA043nVxJQlH7xjEO2RaXxPkRdd1uAXEWrsk0GcHWAnJTRGUVOsy3Qr/In6vIHxDunlJnAFV9ifQuYIsneQyBtgjdR8AmIDTYKqAUaVgYRdfilrshVtiFGYM9AGVvQM8DYrzHbnL+GEam+0QN1nYgoOCJiP1DIDldfV9nmPOJev28ZoOqGbraKZMPzAcun1HTga4EEYeCdZKuJiFgGIJLdgFxkqzVBd//SBTVRVwa43YOIOlwAzP3bIFtSLa1ubNXSNN2SW4k/TAgL9vNVy+ntKVbo7Mu886LK4TAGWNVtbE/vlu3DUTYO2bYvAxYTr7cBmNmYrT9xlgAwCLH2tQDbiLMJyFQT7yzwUMwW84l3CbB42x1GkAfEEm/cCXWzU1IjActw4o0GMExcjWv2oYjZ6vsTby6Ama3JtrsROEo83wyn2eLLxiL1xBkIIJh414B1ZK97VroPceQZb3yPTADMw4g3weQqW4phNhMvrAGIb8z2pkpYC3GhbW8X2WZpiLMcQIMQaziATc1MSTnNljVFnJJykS3SR9huUb0jI95d4GJbZEteW+vAM+kKqwxwwBTcPrI6NrJnkL+vl1ouo1aT971nRrMsTMUL8R0ukxhI75itPYeefgEy5LaPazLFbDcQrx1ju+HvuvUqGswA40jQ0wJMtGZbI7f11s8xWy8Ag1r+ACgdSCKrE8BtcfUjNTnJ9qDdc8xoTba7gcoAEkx3mm2e3Vp+ywVAIQk2v3FOBvmTYAWgI3tqPZBIVjddZkuhZHUbSGzMdoYt0J3uZ7ueeL4G4JZtkH73QLa5viToa4ElnATDAHzaFtmS5jAjZPtk8dpruuoGJ3FVZt5IWr9wUmRo18AAjY+3WiWXkysyuVLt5dM+ILBz6KANOjTDzFRmn1k07Z5RqPZsV/JgttfEQllx4mUSwHQSZANHSLAP+JtYE4E6GVkVAmus2T4kQTCAaMdsSbxz0GW2ajMwiaxiAaNSWO1s12pxJ9Z3gEnhZraOZ3Afp9mKXYaLn/UkAM9sX/kJOTcFQDvHT4VHk9VOl9naHAOuN2Yrnv0XgBT3s40jQZ7tu8ZB4L4Hst1NVguAXLKSNwBz2yRbCjpt7fbhCM3wdVfyK+tNcMaoL3iYqt3508K4L4Z9GhrSMTgoIMBfo9G05/i2Z//NLyAgMDi4a6/+g8fFzFm6MflS+qtKI5yxNFSXZiTP79suylptw5+hsnfOtvaljUNPBbZC6VMA4eJMsu0CcZgEa6wdLBYL5fwFJDXOJFsZgHlvZHsGMO6NUrnOtr94oRaLCRNXu/7McZRQptvZ5gPryaq5mWRBAICJ1u8MVSTYD5wg5yYCaE92xgPoTFbb3cz2AHDTNpNMgt+Au+5nG2u7cB8kwS9Argey/ZmstgIpJMoENrVNttTlnNCtOTO2HXmHLzj6SKdnTGiWhdGXFGQ9TEs9d/Lo/j1b1vMSduzff/jE2Ss30l/kv66sc7W7oa4iN3VDdGcZeUc/FzY03OgjVuuRKSlvC5D/wiobQJxDtldt2a60drAfqH0hqgHuOWZb7yzb7pVgGV4mze7cfLZfA9CQVSCAaPey7QtgpPvZKkzA9+5l217MNhzAKOJ4Zb8ZGo34YcPR8ykp6Y7ZLgKgcDdbr9hl20+mpKTkO8t2z1tle8OW7RoPZ3se0L8QMcDZNsqWnUhkhKCKF7YnIlnQiGXJD4sr6xrMeO9qy3Ku7Z7VV02s9nPzLeA03I6Qkyez7QsH8S1n+yeaeuVWtjTkGQTGrd7NZbvCPtsAAEvcz3am+9l2BfBdK7OlVKA4htv+MlDoT00seA6RQ7YJAOTuZavZUgHRx5jtEzR1u62ypc7WblH5S6CMeAGD5+++9EJXWcMYLXh/6g/GiP+boMz/p3LwmJtitZ7KNgiAdr2dyJazPQ7krrez2L1sST79WA54qc1lO9cx25nuZ/ul+9n6vE22vqkAmOxyAOZRJBJHBob7F09otTcds13qdrbKGwAqbp3VarXZH2W2fwEZ6+3Mb7NsqfPZemtWSb1UJFJ1GTZv07n0vFIuXvPb1euw15NPyUrZI7HOWu2lPjLyXLZiMV+TI9fZ/gzcIXtuZCvSRC3PBTC1mWyHAAghq54APncv288B9LNm+0XL2VI5sKy12ZLyGASOvzb9EAuQFOj8ve0UcYFjtrmO2W4ETAtUxDnQqmzXvq9se7aU7UmX2R4ATpO9tsuWgrW14BlujmxHTfiFTfh+S/L1jDxdub6mjjGa3OnXYjY21Nfoy3V5OgNsGjYGk8B72PUG8OrOhMrI09neB9a0MttpQMVbZCvy0QMHmsk2AMAY+9kcjXvZzrDepqO2ALPdyPYhkNjqbAen6PccO7v7u2HkYB2gk5HzbCMADHoj26VAtWO2j8TH1KpsnwB73ke2AwBEucz2d+C6y2yXAk8/lGwpeF8VeOa8b/2cZKQO7DcyLn5j4umrD14W6MrKK/RVVbW1dUw90yBgGKa+trZar68oLyspznmclnJky5Jvxgz4+m8TRMa0gTLiyDTzss3gVR/qSuTxbI8BqST6zK1sw+2newNC3M1WTlaHgNRmsiUdsJJsp1khucz2V7LaD5QQp9h2Gu1xke1p4KmMBKPdzDYRRRpy6gKQ6nhxFflZ7G6F/F3MNgaAP/HUjJCtygD8QIJj7mebYnuos94l23Z2r/mfTrPdZDvqIKfZTgAagsmqa4e2zZZ8/51t7av+1AgNNUvt3yNy7MTJsbMW/bD8p4Qt2347xDu4a8uWX1cv/2HBjNhJE0YN+ax7ezkJYm6KN0bp/xyuIo7P8OR68MyvVmjoP5BtuBn4Uhx34wl1C9mKy66TwPueLtK9bL/KH2b7JGNfc9kuBSoDiRdcBfzbdbbP1MTrYwA2iU82Uy7ejNh8thMAzCfBYfey7Q/sJedOAX+TYLtjtnQOKPMjXlCFmG04Gu9N+xJCtrI624dSWe5nuwNgOhLv9LtkSzrgMvH8apxmuwDAEOKtd5qt7AmQTIKAzNywts2WvKY9YMAzPvm+m5Kc+0QV2DusW7Cft0JBn/zrH//8178++eQTIvZv//rXP//5j39+QkovTWDX0L6d231CPNmII09Lq+tqSp7vjVARS9555iMjeMzfs73ovWWbFmwTRJxXjT0dBcrjiDO2FkhoKVsx9WQNsQIuAmVezWeb23iYBWYw3xCnZ6WYjJNsVTnA3VBihaUDmUrX2ZovdiLWkFygXnheGwBsU3C5/W56M9taMVt6BNTwD8J7g9m9bMcBzIIhPbp3795FQU2tBUzCCT2p7o1sBwG4LzyjR2YxW3UJUDqYWJGlFiFbegBk8bvK9xpcZ7vSLtsYABc0RKRca3qnbE+L03TBt02N2UbbZdvHDDwOIdaMBqfZ0ngAu7yI1Yl78WRtmy2pRlyohEB/ZESA82q9eg4dMnAAe7eFf/t2ShnXK4uEbj8hmdLL1y+wa+/+EVHDBnhZu6WAib8kJiWuGeNDHM3gnUXiYa6MU9G7cf1LwF0DSjMzOxNRZz2Ae/tWnCoD8ErjTraUCEB36udtf1sghuA821T+MJm9iMJyARSfiv9+fy2Q6dNctjTZAjAXN2+5xACWr8h1trtMTMq2fWkmNN73p6kA8OLQxhNV9U5+SH4C6DIzvbjDgPXk2MaTOmxzL1tfI0Tm4ssjyE5fBmBSl8YsvoFV6UBWZgjZuQyg/tqO3anmiiNitvQdAOPlbbvTcFx8b7sEQOnxmd+syzNPMKI+81Kz2X4NGNhRXS3eDopi7YbDBdjyTtmGGgHc37/lHFN4ojHbPgDYIx0Rv8FDf2bjgWfY4jxbuggg//jqPc8BWMZTG2dLst57Ci3gmZ4tD/WmNynDYmK+GBUV0bdHR39vlVxGYprEp8tm2z6oW5/PokaMnzwlzC5JlZeSeF49Ft1hICg+2EdOHs12GiCezD3uQ/SwO7mVrWyNCVbMImouW7EO4Z1w4B2IqvpTs9nS+BJY6cZSC9kuTIDVXgUJZpsgWOxkSurf4HgTaw4Dwc2+7mVL02pg5zDZWQarjHYzwOpFdnxTILDM3t6YreIWBMUhYraym7DaSacAvGg2W+98cHYTJ1IPwfGx75Qt/QqBKdo2JUU3AYhvsrrkQ3C3RzPZqnZCVBVLbZityO/HpwwEzOVpIao3wu4yI37e9CkTR0eGBnk7TU7GvvcdOPKLydPmfDe3m/yN6jtFayshYJ4t8yfybLY07ykjnszKzfngZC5TkVvZsoalM2DVJw8gV9nSzAzGmi2pN74Gh9kVRC6ypeAzerD0p4OoxWzpx1KwChZSo6hcsEoWkZNsZUteGqzZUtQTMwDmgNrdmWSfFEsFrxacWWQnhj+o8YSGFBvzLdZsRbKEUrDyYsmWLSkSTGBdDyMxW/L6uQ6s8pXc/QKVLrKlnimljdlSyC2w6rcq3jFbiikHKyea7LLtcKSwMVvSnALLnKR5cyZZFP3YyEd7oAd9CNmSatz5UggspYfGdVJQE75fblm/etn3c2IGdlBQM+S+fSbM+m7J6vVbv9Y4rAkauf2VCYKyyxPV9B8WOH7BxH7UKsrP5kwd0p5aQz5o8sKYcB9qUc+42B7kHvXoudHB1ESPmDnDvKllvqMWTAwkd80vYWJI0G5KEXCd7Cn6RC/4shM1p1vczKFyErIVeUfOjenj8IgGfTNntBe1Vsfx88d0oHcn7zN1RqSSXPAfOf+LLuSSOnJ+7GBvog8jW5KFJGQxEKd5t43saJ/nJ903nzp2YNempUN9yQWvfvM37PjtyKkdoU2jjVr3pAECJmdzTxl9aCSfW3CcGk0Cyqn1bNlKPJ6tSB1zocQCgSlry4guahIpR1y/d+vqpeMzfVqa3NpzKTXtXtpYu12Do9b+zcCq9HKcN314JD8Ba+wiBvKkbD+ObEnWbd2TaliZsvd8GSIm5v1Nsa44P+9iOLWg48ZXBcUlr+f5ksCr65iNGQys6l6u7y6jD5BkBXBPbt/fCSnbjyBb8Wp5LI+xfQ5waGpYgIJYvt+ZzSaT4U4YtSBwB2M2m7FUQyy5X2jsgWwDrAyFpyep6IMkiQRwfqCaWL4jkgFMkbL9CLIV+c26XGyElaUiddmI7r5yUk+tB6sw3odcUo/7C6yGeT4k8wkZtvhSmRlWJt1f8Z1l9IGS7AdHX17OgLOEpGw/omxJ1nXJDZ0JIkN20qKobv7DXvLpPZ7agVzwGf47n/er0X4hn889/LIBIlPFvZ/7KenDJVn2EiLDH1+RlO1Hkq1IHrr6ts6IRsyL5PiZV4T6Mlf08aVmqEOmpTLg3Jy+6FBGHRqZyu5vHqSmD5skfO6yzYm71yyYFEBvp8vAgWHUJqRsWYq+P90oYGBjKBbvoqq5NH9AsJMClX6hMYmFEBTlN8CmQXd302AvEkkkUraeIe/1Q0pONZypTUuICe/m5yW3+119Ovcdt+z8awucqMtP/Slc7FwikbL1qOC4Q8/KDHDCXJF+ZOk3Yz8P78saMGhk3OI91wqdbmksf/n7gp4KEkkkUrYe5j0qITVbb4Izllpd1oO069dvpL8sqna+ianq1V9bx/uT50kkUrY2ipDY3TdzKhvQagZ97p1907sryfMkEilbB6qesVv/fF5cY4LbTPW6l1d3TQ9VUVuQSKRsWfKOoxYfvvmySN9gRgssptqSnNvJK8d3UVDbkEikbEWqbuN+3HMp/WVBWbXzeC2GOn1xdsb1Y2vjentR25JIpGxFcr9+4xclHDyf9vB5Zl7h65LSCr2+srREV5SX/SLj3pXk7ctiI7uo6UMhkUjZiuSazv2HR8/+ftnadZt37tq6fu3KH+Z+PW5Qj0DpGvv/7dOxAAAAAMAgf+tJ7CyHYGwLaAtoC9oC2gLagraAtoC2gLagLaAtoC1oC2gLaAvaAtoC2oK2gLaAtqAtoC2gLWgLaAtoC2gL2gLaAtqCtoC2gLagLaAtoC1oC2gLaAvaAtoC2gLagraAtoC2oC2gLaAtaAtoC2gL2gLaAtqCtoC2gLZAy1EI1wwY3vcAAAAASUVORK5CYII="
+    }
+   },
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<img src=\"../../../../../images/qiskit_header.png\" alt=\"Note: In order for images to show up in this jupyter notebook you need to select File => Trusted Notebook\" align=\"middle\">"
+    "![qiskit_header.png](attachment:qiskit_header.png)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# _*Qiskit Finance: qGAN Option Pricing*_ \n",
-    "\n",
-    "The latest version of this notebook is available on https://github.com/Qiskit/qiskit-iqx-tutorials.\n",
-    "\n",
-    "***\n",
-    "### Contributors\n",
-    "Christa Zoufal<sup>[1,2]</sup>, Stefan Woerner<sup>[1]</sup>\n",
-    "### Affliation\n",
-    "- <sup>[1]</sup>IBMQ\n",
-    "- <sup>[2]</sup>ETH Zurich\n",
+    "# Qiskit Finance: qGAN Option Pricing\n",
     "\n",
     "### Introduction\n",
     "In this notebook, we discuss how a Quantum Machine Learning Algorithm, namely a quantum Generative Adversarial Network (qGAN), can facilitate the pricing of a European call option. More specifically, a qGAN can be trained such that a quantum circuit models the spot price of an asset underlying a European call option. The resulting model can then be integrated into a Quantum Amplitude Estimation based algorithm to evaluate the expected payoff - see [European Call Option Pricing](../simulation/european_call_option_pricing.ipynb). <br/>\n",
@@ -29,8 +25,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
+   "execution_count": 7,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:27:46.206138Z",
+     "start_time": "2019-12-12T17:27:46.196112Z"
+    }
+   },
    "outputs": [],
    "source": [
     "import matplotlib.pyplot as plt\n",
@@ -45,7 +46,7 @@
     "\n",
     "from qiskit.aqua import aqua_globals, QuantumInstance\n",
     "\n",
-    "from qiskit import BasicAer"
+    "from qiskit import Aer"
    ]
   },
   {
@@ -64,8 +65,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
+   "execution_count": 8,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:27:46.994621Z",
+     "start_time": "2019-12-12T17:27:46.979019Z"
+    }
+   },
    "outputs": [],
    "source": [
     "# Set upper and lower data values\n",
@@ -104,8 +110,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
+   "execution_count": 9,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:27:47.344815Z",
+     "start_time": "2019-12-12T17:27:47.339695Z"
+    }
+   },
    "outputs": [],
    "source": [
     "# set the strike price (should be within the low and the high value of the uncertainty)\n",
@@ -132,12 +143,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {},
+   "execution_count": 10,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:27:47.967690Z",
+     "start_time": "2019-12-12T17:27:47.707320Z"
+    }
+   },
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -192,22 +208,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {},
+   "execution_count": 11,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:28:08.446992Z",
+     "start_time": "2019-12-12T17:27:48.233523Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Analytically calculated expected payoff w.r.t. the target distribution:  1.0585480647564538\n",
-      "Analytically calculated expected payoff w.r.t. the trained distribution:  0.980530833264945\n",
+      "Analytically calculated expected payoff w.r.t. the target distribution:  1.0654410284943117\n",
+      "Analytically calculated expected payoff w.r.t. the trained distribution:  0.9805308332649448\n",
       "Expected payoff calculated with Quantum Amplitude Estimation: \t1.2580\n",
       "Probability: \t0.8785\n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -231,7 +252,7 @@
     "# construct amplitude estimation \n",
     "ae = AmplitudeEstimation(m, european_call)\n",
     "# result = ae.run(quantum_instance=BasicAer.get_backend('qasm_simulator'), shots=100)\n",
-    "result = ae.run(quantum_instance=BasicAer.get_backend('statevector_simulator'))\n",
+    "result = ae.run(quantum_instance=Aer.get_backend('statevector_simulator'))\n",
     "print('Expected payoff calculated with Quantum Amplitude Estimation: \\t%.4f' % result['estimation'])\n",
     "print('Probability: \\t%.4f' % result['max_probability'])\n",
     "\n",
@@ -250,19 +271,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 12,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2019-08-22T01:54:01.044610Z",
-     "start_time": "2019-08-22T01:54:01.034169Z"
+     "end_time": "2019-12-12T17:28:08.552632Z",
+     "start_time": "2019-12-12T17:28:08.471369Z"
     }
    },
    "outputs": [
     {
      "data": {
       "text/html": [
-       "<h3>Version Information</h3><table><tr><th>Qiskit Software</th><th>Version</th></tr><tr><td>Qiskit</td><td>None</td></tr><tr><td>Terra</td><td>0.9.0</td></tr><tr><td>Aer</td><td>0.3.0</td></tr><tr><td>Ignis</td><td>0.2.0</td></tr><tr><td>Aqua</td><td>0.5.6</td></tr><tr><td>IBM Q Provider</td><td>0.3.2rc1</td></tr><tr><th>System information</th></tr><tr><td>Python</td><td>3.7.4 (default, Aug 13 2019, 15:17:50) \n",
-       "[Clang 4.0.1 (tags/RELEASE_401/final)]</td></tr><tr><td>OS</td><td>Darwin</td></tr><tr><td>CPUs</td><td>4</td></tr><tr><td>Memory (Gb)</td><td>16.0</td></tr><tr><td colspan='2'>Wed Aug 21 21:54:01 2019 EDT</td></tr></table>"
+       "<h3>Version Information</h3><table><tr><th>Qiskit Software</th><th>Version</th></tr><tr><td>Qiskit</td><td>0.14.0</td></tr><tr><td>Terra</td><td>0.11.0</td></tr><tr><td>Aer</td><td>0.3.4</td></tr><tr><td>Ignis</td><td>0.2.0</td></tr><tr><td>Aqua</td><td>0.6.1</td></tr><tr><td>IBM Q Provider</td><td>0.4.4</td></tr><tr><th>System information</th></tr><tr><td>Python</td><td>3.7.5 (default, Oct 25 2019, 10:52:18) \n",
+       "[Clang 4.0.1 (tags/RELEASE_401/final)]</td></tr><tr><td>OS</td><td>Darwin</td></tr><tr><td>CPUs</td><td>4</td></tr><tr><td>Memory (Gb)</td><td>16.0</td></tr><tr><td colspan='2'>Thu Dec 12 12:28:08 2019 EST</td></tr></table>"
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -314,7 +335,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.4"
+   "version": "3.7.5"
   },
   "varInspector": {
    "cols": {
diff --git a/qiskit/advanced/aqua/finance/machine_learning/qgan_option_pricing.ipynb b/qiskit/advanced/aqua/finance/machine_learning/qgan_option_pricing.ipynb
index c999e22e9..79ddd342f 100644
--- a/qiskit/advanced/aqua/finance/machine_learning/qgan_option_pricing.ipynb
+++ b/qiskit/advanced/aqua/finance/machine_learning/qgan_option_pricing.ipynb
@@ -1,26 +1,22 @@
 {
  "cells": [
   {
+   "attachments": {
+    "qiskit_header.png": {
+     "image/png": 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"
+    }
+   },
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<img src=\"../../../../../images/qiskit_header.png\" alt=\"Note: In order for images to show up in this jupyter notebook you need to select File => Trusted Notebook\" align=\"middle\">"
+    "![qiskit_header.png](attachment:qiskit_header.png)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# _*Qiskit Finance: qGAN Option Pricing*_ \n",
-    "\n",
-    "The latest version of this notebook is available on https://github.com/Qiskit/qiskit-iqx-tutorials.\n",
-    "\n",
-    "***\n",
-    "### Contributors\n",
-    "Christa Zoufal<sup>[1,2]</sup>, Stefan Woerner<sup>[1]</sup>\n",
-    "### Affliation\n",
-    "- <sup>[1]</sup>IBMQ\n",
-    "- <sup>[2]</sup>ETH Zurich\n",
+    "# Qiskit Finance: qGAN Option Pricing\n",
     "\n",
     "### Introduction\n",
     "In this notebook, we discuss how a Quantum Machine Learning Algorithm, namely a quantum Generative Adversarial Network (qGAN), can facilitate the pricing of a European call option. More specifically, a qGAN can be trained such that a quantum circuit models the spot price of an asset underlying a European call option. The resulting model can then be integrated into a Quantum Amplitude Estimation based algorithm to evaluate the expected payoff - see [European Call Option Pricing](../simulation/european_call_option_pricing.ipynb). <br/>\n",
@@ -29,8 +25,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
+   "execution_count": 7,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:27:46.206138Z",
+     "start_time": "2019-12-12T17:27:46.196112Z"
+    }
+   },
    "outputs": [],
    "source": [
     "import matplotlib.pyplot as plt\n",
@@ -45,7 +46,7 @@
     "\n",
     "from qiskit.aqua import aqua_globals, QuantumInstance\n",
     "\n",
-    "from qiskit import BasicAer"
+    "from qiskit import Aer"
    ]
   },
   {
@@ -64,8 +65,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
+   "execution_count": 8,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:27:46.994621Z",
+     "start_time": "2019-12-12T17:27:46.979019Z"
+    }
+   },
    "outputs": [],
    "source": [
     "# Set upper and lower data values\n",
@@ -104,8 +110,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
+   "execution_count": 9,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:27:47.344815Z",
+     "start_time": "2019-12-12T17:27:47.339695Z"
+    }
+   },
    "outputs": [],
    "source": [
     "# set the strike price (should be within the low and the high value of the uncertainty)\n",
@@ -132,12 +143,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {},
+   "execution_count": 10,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:27:47.967690Z",
+     "start_time": "2019-12-12T17:27:47.707320Z"
+    }
+   },
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -192,22 +208,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {},
+   "execution_count": 11,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:28:08.446992Z",
+     "start_time": "2019-12-12T17:27:48.233523Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Analytically calculated expected payoff w.r.t. the target distribution:  1.0585480647564538\n",
-      "Analytically calculated expected payoff w.r.t. the trained distribution:  0.980530833264945\n",
+      "Analytically calculated expected payoff w.r.t. the target distribution:  1.0654410284943117\n",
+      "Analytically calculated expected payoff w.r.t. the trained distribution:  0.9805308332649448\n",
       "Expected payoff calculated with Quantum Amplitude Estimation: \t1.2580\n",
       "Probability: \t0.8785\n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -231,7 +252,7 @@
     "# construct amplitude estimation \n",
     "ae = AmplitudeEstimation(m, european_call)\n",
     "# result = ae.run(quantum_instance=BasicAer.get_backend('qasm_simulator'), shots=100)\n",
-    "result = ae.run(quantum_instance=BasicAer.get_backend('statevector_simulator'))\n",
+    "result = ae.run(quantum_instance=Aer.get_backend('statevector_simulator'))\n",
     "print('Expected payoff calculated with Quantum Amplitude Estimation: \\t%.4f' % result['estimation'])\n",
     "print('Probability: \\t%.4f' % result['max_probability'])\n",
     "\n",
@@ -250,19 +271,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 12,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2019-08-22T01:54:01.044610Z",
-     "start_time": "2019-08-22T01:54:01.034169Z"
+     "end_time": "2019-12-12T17:28:08.552632Z",
+     "start_time": "2019-12-12T17:28:08.471369Z"
     }
    },
    "outputs": [
     {
      "data": {
       "text/html": [
-       "<h3>Version Information</h3><table><tr><th>Qiskit Software</th><th>Version</th></tr><tr><td>Qiskit</td><td>None</td></tr><tr><td>Terra</td><td>0.9.0</td></tr><tr><td>Aer</td><td>0.3.0</td></tr><tr><td>Ignis</td><td>0.2.0</td></tr><tr><td>Aqua</td><td>0.5.6</td></tr><tr><td>IBM Q Provider</td><td>0.3.2rc1</td></tr><tr><th>System information</th></tr><tr><td>Python</td><td>3.7.4 (default, Aug 13 2019, 15:17:50) \n",
-       "[Clang 4.0.1 (tags/RELEASE_401/final)]</td></tr><tr><td>OS</td><td>Darwin</td></tr><tr><td>CPUs</td><td>4</td></tr><tr><td>Memory (Gb)</td><td>16.0</td></tr><tr><td colspan='2'>Wed Aug 21 21:54:01 2019 EDT</td></tr></table>"
+       "<h3>Version Information</h3><table><tr><th>Qiskit Software</th><th>Version</th></tr><tr><td>Qiskit</td><td>0.14.0</td></tr><tr><td>Terra</td><td>0.11.0</td></tr><tr><td>Aer</td><td>0.3.4</td></tr><tr><td>Ignis</td><td>0.2.0</td></tr><tr><td>Aqua</td><td>0.6.1</td></tr><tr><td>IBM Q Provider</td><td>0.4.4</td></tr><tr><th>System information</th></tr><tr><td>Python</td><td>3.7.5 (default, Oct 25 2019, 10:52:18) \n",
+       "[Clang 4.0.1 (tags/RELEASE_401/final)]</td></tr><tr><td>OS</td><td>Darwin</td></tr><tr><td>CPUs</td><td>4</td></tr><tr><td>Memory (Gb)</td><td>16.0</td></tr><tr><td colspan='2'>Thu Dec 12 12:28:08 2019 EST</td></tr></table>"
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -314,7 +335,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.4"
+   "version": "3.7.5"
   },
   "varInspector": {
    "cols": {
diff --git a/qiskit/advanced/aqua/finance/optimization/.ipynb_checkpoints/portfolio_diversification-checkpoint.ipynb b/qiskit/advanced/aqua/finance/optimization/.ipynb_checkpoints/portfolio_diversification-checkpoint.ipynb
index 519738793..d10180c74 100644
--- a/qiskit/advanced/aqua/finance/optimization/.ipynb_checkpoints/portfolio_diversification-checkpoint.ipynb
+++ b/qiskit/advanced/aqua/finance/optimization/.ipynb_checkpoints/portfolio_diversification-checkpoint.ipynb
@@ -1,26 +1,22 @@
 {
  "cells": [
   {
+   "attachments": {
+    "qiskit_header.png": {
+     "image/png": 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"
+    }
+   },
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<img src=\"../../../../../images/qiskit_header.png\" alt=\"Note: In order for images to show up in this jupyter notebook you need to select File => Trusted Notebook\" align=\"middle\">"
+    "![qiskit_header.png](attachment:qiskit_header.png)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# _*Qiskit Finance: Portfolio diversification*_\n",
-    "\n",
-    "The latest version of this notebook is available on https://github.com/qiskit/qiskit-tutorial.\n",
-    "\n",
-    "***\n",
-    "### Contributors\n",
-    "Andrea Simonetto<sup>[1]</sup>, Jakub Marecek<sup>[1]</sup>, Martin Mevissen<sup>[1]</sup>\n",
-    "\n",
-    "### Affiliation\n",
-    "- <sup>[1]</sup>IBMQ\n",
+    "# Qiskit Finance: Portfolio diversification\n",
     "\n",
     "\n",
     "## Introduction \n",
@@ -192,8 +188,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
+   "execution_count": 12,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:30:23.350237Z",
+     "start_time": "2019-12-12T17:30:23.338139Z"
+    }
+   },
    "outputs": [],
    "source": [
     "# Import requisite modules\n",
@@ -213,7 +214,7 @@
     "# Import Qiskit packages\n",
     "warnings.filterwarnings('ignore')\n",
     "import qiskit \n",
-    "from qiskit import BasicAer\n",
+    "from qiskit import Aer\n",
     "from qiskit.aqua import QuantumInstance\n",
     "from qiskit.aqua import Operator, run_algorithm\n",
     "from qiskit.aqua.input import EnergyInput\n",
@@ -238,8 +239,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 13,
    "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:30:23.720393Z",
+     "start_time": "2019-12-12T17:30:23.711527Z"
+    },
     "scrolled": true
    },
    "outputs": [],
@@ -270,8 +275,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
+   "execution_count": 14,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:30:24.106813Z",
+     "start_time": "2019-12-12T17:30:24.103335Z"
+    }
+   },
    "outputs": [],
    "source": [
     "q = 1  # q less or equal than n"
@@ -288,8 +298,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
+   "execution_count": 15,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:30:24.490620Z",
+     "start_time": "2019-12-12T17:30:24.469064Z"
+    }
+   },
    "outputs": [],
    "source": [
     "class ClassicalOptimizer:\n",
@@ -375,8 +390,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
+   "execution_count": 16,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:30:24.638151Z",
+     "start_time": "2019-12-12T17:30:24.632360Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -400,8 +420,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
+   "execution_count": 17,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:30:24.810187Z",
+     "start_time": "2019-12-12T17:30:24.799368Z"
+    }
+   },
    "outputs": [],
    "source": [
     "# Visualize the solution\n",
@@ -451,8 +476,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
+   "execution_count": 18,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:30:25.342845Z",
+     "start_time": "2019-12-12T17:30:25.330848Z"
+    }
+   },
    "outputs": [],
    "source": [
     "class QuantumOptimizer:\n",
@@ -479,7 +509,7 @@
     "\n",
     "    def vqe_solution(self):\n",
     "        qubitOp = portfolio_diversification.get_portfoliodiversification_qubitops(self.rho, self.n, self.q)\n",
-    "        backend = BasicAer.get_backend('statevector_simulator')\n",
+    "        backend = Aer.get_backend('statevector_simulator')\n",
     "        seed = 50\n",
     "        cobyla = COBYLA()\n",
     "        cobyla.set_options(maxiter=250)\n",
@@ -492,7 +522,7 @@
     "        \n",
     "    def qaoa_solution(self):\n",
     "        qubitOp = portfolio_diversification.get_portfoliodiversification_qubitops(self.rho, self.n, self.q)\n",
-    "        backend = BasicAer.get_backend('statevector_simulator')\n",
+    "        backend = Aer.get_backend('statevector_simulator')\n",
     "        seed = 50\n",
     "        cobyla = COBYLA()\n",
     "        cobyla.set_options(maxiter=250)\n",
@@ -521,8 +551,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
+   "execution_count": 19,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:30:26.073792Z",
+     "start_time": "2019-12-12T17:30:26.068421Z"
+    }
+   },
    "outputs": [],
    "source": [
     "# Instantiate the quantum optimizer class with parameters: \n",
@@ -542,8 +577,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
+   "execution_count": 20,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:30:27.037750Z",
+     "start_time": "2019-12-12T17:30:27.032451Z"
+    }
+   },
    "outputs": [],
    "source": [
     "# Check if the binary representation is correct. This requires CPLEX\n",
@@ -572,8 +612,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
+   "execution_count": 21,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:30:27.942971Z",
+     "start_time": "2019-12-12T17:30:27.896698Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -605,15 +650,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
+   "execution_count": 22,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:30:52.929193Z",
+     "start_time": "2019-12-12T17:30:28.518265Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[1 0 1 0 1 0]\n",
-      "VQE does not produce the same solution as the exact eigensolver, but that is to be expected.\n"
+      "[0 1 0 1 0 1]\n",
+      "VQE produces the same solution as the exact eigensolver.\n"
      ]
     }
    ],
@@ -639,12 +689,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
+   "execution_count": 23,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:30:53.362901Z",
+     "start_time": "2019-12-12T17:30:52.955535Z"
+    }
+   },
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -656,7 +711,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -682,19 +737,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 24,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2019-08-22T01:54:27.014553Z",
-     "start_time": "2019-08-22T01:54:27.005205Z"
+     "end_time": "2019-12-12T17:30:53.500539Z",
+     "start_time": "2019-12-12T17:30:53.399526Z"
     }
    },
    "outputs": [
     {
      "data": {
       "text/html": [
-       "<h3>Version Information</h3><table><tr><th>Qiskit Software</th><th>Version</th></tr><tr><td>Qiskit</td><td>None</td></tr><tr><td>Terra</td><td>0.9.0</td></tr><tr><td>Aer</td><td>0.3.0</td></tr><tr><td>Ignis</td><td>0.2.0</td></tr><tr><td>Aqua</td><td>0.5.6</td></tr><tr><td>IBM Q Provider</td><td>0.3.2rc1</td></tr><tr><th>System information</th></tr><tr><td>Python</td><td>3.7.4 (default, Aug 13 2019, 15:17:50) \n",
-       "[Clang 4.0.1 (tags/RELEASE_401/final)]</td></tr><tr><td>OS</td><td>Darwin</td></tr><tr><td>CPUs</td><td>4</td></tr><tr><td>Memory (Gb)</td><td>16.0</td></tr><tr><td colspan='2'>Wed Aug 21 21:54:27 2019 EDT</td></tr></table>"
+       "<h3>Version Information</h3><table><tr><th>Qiskit Software</th><th>Version</th></tr><tr><td>Qiskit</td><td>0.14.0</td></tr><tr><td>Terra</td><td>0.11.0</td></tr><tr><td>Aer</td><td>0.3.4</td></tr><tr><td>Ignis</td><td>0.2.0</td></tr><tr><td>Aqua</td><td>0.6.1</td></tr><tr><td>IBM Q Provider</td><td>0.4.4</td></tr><tr><th>System information</th></tr><tr><td>Python</td><td>3.7.5 (default, Oct 25 2019, 10:52:18) \n",
+       "[Clang 4.0.1 (tags/RELEASE_401/final)]</td></tr><tr><td>OS</td><td>Darwin</td></tr><tr><td>CPUs</td><td>4</td></tr><tr><td>Memory (Gb)</td><td>16.0</td></tr><tr><td colspan='2'>Thu Dec 12 12:30:53 2019 EST</td></tr></table>"
       ],
       "text/plain": [
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@@ -746,7 +801,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.4"
+   "version": "3.7.5"
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diff --git a/qiskit/advanced/aqua/finance/optimization/.ipynb_checkpoints/portfolio_optimization-checkpoint.ipynb b/qiskit/advanced/aqua/finance/optimization/.ipynb_checkpoints/portfolio_optimization-checkpoint.ipynb
index 4f1ca589d..20d08671b 100644
--- a/qiskit/advanced/aqua/finance/optimization/.ipynb_checkpoints/portfolio_optimization-checkpoint.ipynb
+++ b/qiskit/advanced/aqua/finance/optimization/.ipynb_checkpoints/portfolio_optimization-checkpoint.ipynb
@@ -1,10 +1,15 @@
 {
  "cells": [
   {
+   "attachments": {
+    "qiskit_header.png": {
+     "image/png": 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"
+    }
+   },
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<img src=\"../../../../../images/qiskit_header.png\" alt=\"Note: In order for images to show up in this jupyter notebook you need to select File => Trusted Notebook\" align=\"middle\">"
+    "![qiskit_header.png](attachment:qiskit_header.png)"
    ]
   },
   {
@@ -13,15 +18,7 @@
     "collapsed": true
    },
    "source": [
-    "# _*Qiskit Finance: Portfolio Optimization*_ \n",
-    "\n",
-    "The latest version of this notebook is available on https://github.com/Qiskit/qiskit-iqx-tutorials.\n",
-    "\n",
-    "***\n",
-    "### Contributors\n",
-    "Stefan Woerner<sup>[1]</sup>, Daniel Egger<sup>[1]</sup>, Shaohan Hu<sup>[1]</sup>, Stephen Wood<sup>[1]</sup>, Marco Pistoia<sup>[1]</sup>\n",
-    "### Affliation\n",
-    "- <sup>[1]</sup>IBMQ"
+    "# Qiskit Finance: Portfolio Optimization"
    ]
   },
   {
@@ -60,11 +57,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
+   "execution_count": 15,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:32:27.923672Z",
+     "start_time": "2019-12-12T17:32:27.917959Z"
+    }
+   },
    "outputs": [],
    "source": [
-    "from qiskit import BasicAer\n",
+    "from qiskit import Aer\n",
     "from qiskit.aqua import QuantumInstance\n",
     "from qiskit.aqua import Operator, run_algorithm\n",
     "from qiskit.aqua.input import EnergyInput\n",
@@ -89,9 +91,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 16,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:32:36.256007Z",
+     "start_time": "2019-12-12T17:32:28.449895Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Credentials are already in use. The existing account in the session will be replaced.\n"
+     ]
+    }
+   ],
    "source": [
     "from qiskit import IBMQ\n",
     "provider = IBMQ.load_account()"
@@ -109,8 +124,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
+   "execution_count": 17,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:32:36.283153Z",
+     "start_time": "2019-12-12T17:32:36.273501Z"
+    }
+   },
    "outputs": [],
    "source": [
     "# set number of assets (= number of qubits)\n",
@@ -128,8 +148,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
+   "execution_count": 18,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:32:36.299281Z",
+     "start_time": "2019-12-12T17:32:36.293397Z"
+    }
+   },
    "outputs": [],
    "source": [
     "q = 0.5 # set risk factor\n",
@@ -149,8 +174,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
+   "execution_count": 19,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:32:36.319481Z",
+     "start_time": "2019-12-12T17:32:36.311062Z"
+    }
+   },
    "outputs": [],
    "source": [
     "def index_to_selection(i, num_assets):\n",
@@ -187,33 +217,38 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
+   "execution_count": 20,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:32:36.348016Z",
+     "start_time": "2019-12-12T17:32:36.333251Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Optimal: selection [1 1 0 0], value -0.0068\n",
+      "Optimal: selection [0 1 0 1], value -0.0088\n",
       "\n",
       "----------------- Full result ---------------------\n",
       "selection\tvalue\t\tprobability\n",
       "---------------------------------------------------\n",
-      " [1 1 0 0]\t-0.0068\t\t1.0000\n",
-      " [1 1 1 1]\t15.9945\t\t0.0000\n",
-      " [0 1 1 1]\t3.9954\t\t0.0000\n",
-      " [1 0 1 1]\t4.0000\t\t0.0000\n",
-      " [0 0 1 1]\t0.0010\t\t0.0000\n",
-      " [1 1 0 1]\t3.9926\t\t0.0000\n",
-      " [0 1 0 1]\t-0.0065\t\t0.0000\n",
-      " [1 0 0 1]\t-0.0017\t\t0.0000\n",
-      " [0 0 0 1]\t3.9993\t\t0.0000\n",
-      " [1 1 1 0]\t3.9951\t\t0.0000\n",
-      " [0 1 1 0]\t-0.0039\t\t0.0000\n",
-      " [1 0 1 0]\t0.0007\t\t0.0000\n",
-      " [0 0 1 0]\t4.0017\t\t0.0000\n",
-      " [0 1 0 0]\t3.9942\t\t0.0000\n",
-      " [1 0 0 0]\t3.9990\t\t0.0000\n",
+      " [0 1 0 1]\t-0.0088\t\t1.0000\n",
+      " [1 1 1 1]\t15.9908\t\t0.0000\n",
+      " [0 1 1 1]\t3.9906\t\t0.0000\n",
+      " [1 0 1 1]\t3.9967\t\t0.0000\n",
+      " [0 0 1 1]\t-0.0036\t\t0.0000\n",
+      " [1 1 0 1]\t3.9914\t\t0.0000\n",
+      " [1 0 0 1]\t-0.0026\t\t0.0000\n",
+      " [0 0 0 1]\t3.9971\t\t0.0000\n",
+      " [1 1 1 0]\t3.9938\t\t0.0000\n",
+      " [0 1 1 0]\t-0.0064\t\t0.0000\n",
+      " [1 0 1 0]\t-0.0005\t\t0.0000\n",
+      " [0 0 1 0]\t3.9993\t\t0.0000\n",
+      " [1 1 0 0]\t-0.0055\t\t0.0000\n",
+      " [0 1 0 0]\t3.9944\t\t0.0000\n",
+      " [1 0 0 0]\t4.0003\t\t0.0000\n",
       " [0 0 0 0]\t16.0000\t\t0.0000\n"
      ]
     }
@@ -249,39 +284,44 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
+   "execution_count": 21,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:32:52.517267Z",
+     "start_time": "2019-12-12T17:32:36.362801Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Optimal: selection [0 1 1 0], value -0.0039\n",
+      "Optimal: selection [0 1 1 0], value -0.0064\n",
       "\n",
       "----------------- Full result ---------------------\n",
       "selection\tvalue\t\tprobability\n",
       "---------------------------------------------------\n",
-      " [0 1 1 0]\t-0.0039\t\t0.8805\n",
-      " [0 0 1 1]\t0.0010\t\t0.1186\n",
-      " [1 1 0 0]\t-0.0068\t\t0.0008\n",
-      " [1 0 0 1]\t-0.0017\t\t0.0001\n",
-      " [1 0 1 0]\t0.0007\t\t0.0001\n",
-      " [0 1 0 1]\t-0.0065\t\t0.0000\n",
-      " [0 0 0 1]\t3.9993\t\t0.0000\n",
-      " [1 1 0 1]\t3.9926\t\t0.0000\n",
-      " [0 1 0 0]\t3.9942\t\t0.0000\n",
-      " [1 0 1 1]\t4.0000\t\t0.0000\n",
-      " [1 1 1 1]\t15.9945\t\t0.0000\n",
-      " [1 0 0 0]\t3.9990\t\t0.0000\n",
-      " [0 0 1 0]\t4.0017\t\t0.0000\n",
-      " [0 1 1 1]\t3.9954\t\t0.0000\n",
+      " [0 1 1 0]\t-0.0064\t\t0.6809\n",
+      " [0 0 1 1]\t-0.0036\t\t0.3086\n",
+      " [1 0 1 0]\t-0.0005\t\t0.0061\n",
+      " [1 1 0 0]\t-0.0055\t\t0.0027\n",
+      " [1 0 0 1]\t-0.0026\t\t0.0009\n",
+      " [0 1 0 1]\t-0.0088\t\t0.0004\n",
+      " [1 1 0 1]\t3.9914\t\t0.0002\n",
+      " [1 0 0 0]\t4.0003\t\t0.0001\n",
+      " [0 0 0 1]\t3.9971\t\t0.0000\n",
+      " [0 1 0 0]\t3.9944\t\t0.0000\n",
+      " [1 0 1 1]\t3.9967\t\t0.0000\n",
+      " [1 1 1 0]\t3.9938\t\t0.0000\n",
       " [0 0 0 0]\t16.0000\t\t0.0000\n",
-      " [1 1 1 0]\t3.9951\t\t0.0000\n"
+      " [0 0 1 0]\t3.9993\t\t0.0000\n",
+      " [1 1 1 1]\t15.9908\t\t0.0000\n",
+      " [0 1 1 1]\t3.9906\t\t0.0000\n"
      ]
     }
    ],
    "source": [
-    "backend = BasicAer.get_backend('statevector_simulator')\n",
+    "backend = Aer.get_backend('statevector_simulator')\n",
     "seed = 50\n",
     "\n",
     "cobyla = COBYLA()\n",
@@ -333,39 +373,44 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
+   "execution_count": 22,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:32:54.578898Z",
+     "start_time": "2019-12-12T17:32:52.527240Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Optimal: selection [0 0 1 1], value 0.0010\n",
+      "Optimal: selection [0 0 0 0], value 16.0000\n",
       "\n",
       "----------------- Full result ---------------------\n",
       "selection\tvalue\t\tprobability\n",
       "---------------------------------------------------\n",
-      " [0 0 1 1]\t0.0010\t\t0.1668\n",
-      " [1 0 1 0]\t0.0007\t\t0.1668\n",
-      " [1 0 0 1]\t-0.0017\t\t0.1667\n",
-      " [0 1 1 0]\t-0.0039\t\t0.1666\n",
-      " [0 1 0 1]\t-0.0065\t\t0.1665\n",
-      " [1 1 0 0]\t-0.0068\t\t0.1665\n",
-      " [0 0 1 0]\t4.0017\t\t0.0000\n",
-      " [1 0 1 1]\t4.0000\t\t0.0000\n",
-      " [1 1 1 1]\t15.9945\t\t0.0000\n",
-      " [0 0 0 1]\t3.9993\t\t0.0000\n",
-      " [1 0 0 0]\t3.9990\t\t0.0000\n",
-      " [0 1 0 0]\t3.9942\t\t0.0000\n",
-      " [1 1 0 1]\t3.9926\t\t0.0000\n",
-      " [0 1 1 1]\t3.9954\t\t0.0000\n",
-      " [1 1 1 0]\t3.9951\t\t0.0000\n",
-      " [0 0 0 0]\t16.0000\t\t0.0000\n"
+      " [0 0 0 1]\t3.9971\t\t0.0625\n",
+      " [0 0 1 0]\t3.9993\t\t0.0625\n",
+      " [0 1 0 0]\t3.9944\t\t0.0625\n",
+      " [1 0 0 0]\t4.0003\t\t0.0625\n",
+      " [0 0 0 0]\t16.0000\t\t0.0625\n",
+      " [0 0 1 1]\t-0.0036\t\t0.0625\n",
+      " [0 1 0 1]\t-0.0088\t\t0.0625\n",
+      " [1 0 0 1]\t-0.0026\t\t0.0625\n",
+      " [0 1 1 0]\t-0.0064\t\t0.0625\n",
+      " [1 0 1 0]\t-0.0005\t\t0.0625\n",
+      " [1 1 0 0]\t-0.0055\t\t0.0625\n",
+      " [0 1 1 1]\t3.9906\t\t0.0625\n",
+      " [1 0 1 1]\t3.9967\t\t0.0625\n",
+      " [1 1 0 1]\t3.9914\t\t0.0625\n",
+      " [1 1 1 0]\t3.9938\t\t0.0625\n",
+      " [1 1 1 1]\t15.9908\t\t0.0625\n"
      ]
     }
    ],
    "source": [
-    "backend = BasicAer.get_backend('statevector_simulator')\n",
+    "backend = Aer.get_backend('statevector_simulator')\n",
     "seed = 50\n",
     "\n",
     "cobyla = COBYLA()\n",
@@ -402,19 +447,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 23,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2019-08-22T01:54:45.323683Z",
-     "start_time": "2019-08-22T01:54:45.313579Z"
+     "end_time": "2019-12-12T17:32:54.597679Z",
+     "start_time": "2019-12-12T17:32:54.590297Z"
     }
    },
    "outputs": [
     {
      "data": {
       "text/html": [
-       "<h3>Version Information</h3><table><tr><th>Qiskit Software</th><th>Version</th></tr><tr><td>Qiskit</td><td>None</td></tr><tr><td>Terra</td><td>0.9.0</td></tr><tr><td>Aer</td><td>0.3.0</td></tr><tr><td>Ignis</td><td>0.2.0</td></tr><tr><td>Aqua</td><td>0.5.6</td></tr><tr><td>IBM Q Provider</td><td>0.3.2rc1</td></tr><tr><th>System information</th></tr><tr><td>Python</td><td>3.7.4 (default, Aug 13 2019, 15:17:50) \n",
-       "[Clang 4.0.1 (tags/RELEASE_401/final)]</td></tr><tr><td>OS</td><td>Darwin</td></tr><tr><td>CPUs</td><td>4</td></tr><tr><td>Memory (Gb)</td><td>16.0</td></tr><tr><td colspan='2'>Wed Aug 21 21:54:45 2019 EDT</td></tr></table>"
+       "<h3>Version Information</h3><table><tr><th>Qiskit Software</th><th>Version</th></tr><tr><td>Qiskit</td><td>0.14.0</td></tr><tr><td>Terra</td><td>0.11.0</td></tr><tr><td>Aer</td><td>0.3.4</td></tr><tr><td>Ignis</td><td>0.2.0</td></tr><tr><td>Aqua</td><td>0.6.1</td></tr><tr><td>IBM Q Provider</td><td>0.4.4</td></tr><tr><th>System information</th></tr><tr><td>Python</td><td>3.7.5 (default, Oct 25 2019, 10:52:18) \n",
+       "[Clang 4.0.1 (tags/RELEASE_401/final)]</td></tr><tr><td>OS</td><td>Darwin</td></tr><tr><td>CPUs</td><td>4</td></tr><tr><td>Memory (Gb)</td><td>16.0</td></tr><tr><td colspan='2'>Thu Dec 12 12:32:54 2019 EST</td></tr></table>"
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -466,7 +511,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.4"
+   "version": "3.7.5"
   },
   "varInspector": {
    "cols": {
diff --git a/qiskit/advanced/aqua/finance/optimization/portfolio_diversification.ipynb b/qiskit/advanced/aqua/finance/optimization/portfolio_diversification.ipynb
index 519738793..d10180c74 100644
--- a/qiskit/advanced/aqua/finance/optimization/portfolio_diversification.ipynb
+++ b/qiskit/advanced/aqua/finance/optimization/portfolio_diversification.ipynb
@@ -1,26 +1,22 @@
 {
  "cells": [
   {
+   "attachments": {
+    "qiskit_header.png": {
+     "image/png": 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"
+    }
+   },
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<img src=\"../../../../../images/qiskit_header.png\" alt=\"Note: In order for images to show up in this jupyter notebook you need to select File => Trusted Notebook\" align=\"middle\">"
+    "![qiskit_header.png](attachment:qiskit_header.png)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# _*Qiskit Finance: Portfolio diversification*_\n",
-    "\n",
-    "The latest version of this notebook is available on https://github.com/qiskit/qiskit-tutorial.\n",
-    "\n",
-    "***\n",
-    "### Contributors\n",
-    "Andrea Simonetto<sup>[1]</sup>, Jakub Marecek<sup>[1]</sup>, Martin Mevissen<sup>[1]</sup>\n",
-    "\n",
-    "### Affiliation\n",
-    "- <sup>[1]</sup>IBMQ\n",
+    "# Qiskit Finance: Portfolio diversification\n",
     "\n",
     "\n",
     "## Introduction \n",
@@ -192,8 +188,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
+   "execution_count": 12,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:30:23.350237Z",
+     "start_time": "2019-12-12T17:30:23.338139Z"
+    }
+   },
    "outputs": [],
    "source": [
     "# Import requisite modules\n",
@@ -213,7 +214,7 @@
     "# Import Qiskit packages\n",
     "warnings.filterwarnings('ignore')\n",
     "import qiskit \n",
-    "from qiskit import BasicAer\n",
+    "from qiskit import Aer\n",
     "from qiskit.aqua import QuantumInstance\n",
     "from qiskit.aqua import Operator, run_algorithm\n",
     "from qiskit.aqua.input import EnergyInput\n",
@@ -238,8 +239,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 13,
    "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:30:23.720393Z",
+     "start_time": "2019-12-12T17:30:23.711527Z"
+    },
     "scrolled": true
    },
    "outputs": [],
@@ -270,8 +275,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
+   "execution_count": 14,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:30:24.106813Z",
+     "start_time": "2019-12-12T17:30:24.103335Z"
+    }
+   },
    "outputs": [],
    "source": [
     "q = 1  # q less or equal than n"
@@ -288,8 +298,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
+   "execution_count": 15,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:30:24.490620Z",
+     "start_time": "2019-12-12T17:30:24.469064Z"
+    }
+   },
    "outputs": [],
    "source": [
     "class ClassicalOptimizer:\n",
@@ -375,8 +390,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
+   "execution_count": 16,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:30:24.638151Z",
+     "start_time": "2019-12-12T17:30:24.632360Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -400,8 +420,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
+   "execution_count": 17,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:30:24.810187Z",
+     "start_time": "2019-12-12T17:30:24.799368Z"
+    }
+   },
    "outputs": [],
    "source": [
     "# Visualize the solution\n",
@@ -451,8 +476,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
+   "execution_count": 18,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:30:25.342845Z",
+     "start_time": "2019-12-12T17:30:25.330848Z"
+    }
+   },
    "outputs": [],
    "source": [
     "class QuantumOptimizer:\n",
@@ -479,7 +509,7 @@
     "\n",
     "    def vqe_solution(self):\n",
     "        qubitOp = portfolio_diversification.get_portfoliodiversification_qubitops(self.rho, self.n, self.q)\n",
-    "        backend = BasicAer.get_backend('statevector_simulator')\n",
+    "        backend = Aer.get_backend('statevector_simulator')\n",
     "        seed = 50\n",
     "        cobyla = COBYLA()\n",
     "        cobyla.set_options(maxiter=250)\n",
@@ -492,7 +522,7 @@
     "        \n",
     "    def qaoa_solution(self):\n",
     "        qubitOp = portfolio_diversification.get_portfoliodiversification_qubitops(self.rho, self.n, self.q)\n",
-    "        backend = BasicAer.get_backend('statevector_simulator')\n",
+    "        backend = Aer.get_backend('statevector_simulator')\n",
     "        seed = 50\n",
     "        cobyla = COBYLA()\n",
     "        cobyla.set_options(maxiter=250)\n",
@@ -521,8 +551,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
+   "execution_count": 19,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:30:26.073792Z",
+     "start_time": "2019-12-12T17:30:26.068421Z"
+    }
+   },
    "outputs": [],
    "source": [
     "# Instantiate the quantum optimizer class with parameters: \n",
@@ -542,8 +577,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
+   "execution_count": 20,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:30:27.037750Z",
+     "start_time": "2019-12-12T17:30:27.032451Z"
+    }
+   },
    "outputs": [],
    "source": [
     "# Check if the binary representation is correct. This requires CPLEX\n",
@@ -572,8 +612,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
+   "execution_count": 21,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:30:27.942971Z",
+     "start_time": "2019-12-12T17:30:27.896698Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -605,15 +650,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
+   "execution_count": 22,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:30:52.929193Z",
+     "start_time": "2019-12-12T17:30:28.518265Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[1 0 1 0 1 0]\n",
-      "VQE does not produce the same solution as the exact eigensolver, but that is to be expected.\n"
+      "[0 1 0 1 0 1]\n",
+      "VQE produces the same solution as the exact eigensolver.\n"
      ]
     }
    ],
@@ -639,12 +689,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
+   "execution_count": 23,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:30:53.362901Z",
+     "start_time": "2019-12-12T17:30:52.955535Z"
+    }
+   },
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -656,7 +711,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -682,19 +737,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 24,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2019-08-22T01:54:27.014553Z",
-     "start_time": "2019-08-22T01:54:27.005205Z"
+     "end_time": "2019-12-12T17:30:53.500539Z",
+     "start_time": "2019-12-12T17:30:53.399526Z"
     }
    },
    "outputs": [
     {
      "data": {
       "text/html": [
-       "<h3>Version Information</h3><table><tr><th>Qiskit Software</th><th>Version</th></tr><tr><td>Qiskit</td><td>None</td></tr><tr><td>Terra</td><td>0.9.0</td></tr><tr><td>Aer</td><td>0.3.0</td></tr><tr><td>Ignis</td><td>0.2.0</td></tr><tr><td>Aqua</td><td>0.5.6</td></tr><tr><td>IBM Q Provider</td><td>0.3.2rc1</td></tr><tr><th>System information</th></tr><tr><td>Python</td><td>3.7.4 (default, Aug 13 2019, 15:17:50) \n",
-       "[Clang 4.0.1 (tags/RELEASE_401/final)]</td></tr><tr><td>OS</td><td>Darwin</td></tr><tr><td>CPUs</td><td>4</td></tr><tr><td>Memory (Gb)</td><td>16.0</td></tr><tr><td colspan='2'>Wed Aug 21 21:54:27 2019 EDT</td></tr></table>"
+       "<h3>Version Information</h3><table><tr><th>Qiskit Software</th><th>Version</th></tr><tr><td>Qiskit</td><td>0.14.0</td></tr><tr><td>Terra</td><td>0.11.0</td></tr><tr><td>Aer</td><td>0.3.4</td></tr><tr><td>Ignis</td><td>0.2.0</td></tr><tr><td>Aqua</td><td>0.6.1</td></tr><tr><td>IBM Q Provider</td><td>0.4.4</td></tr><tr><th>System information</th></tr><tr><td>Python</td><td>3.7.5 (default, Oct 25 2019, 10:52:18) \n",
+       "[Clang 4.0.1 (tags/RELEASE_401/final)]</td></tr><tr><td>OS</td><td>Darwin</td></tr><tr><td>CPUs</td><td>4</td></tr><tr><td>Memory (Gb)</td><td>16.0</td></tr><tr><td colspan='2'>Thu Dec 12 12:30:53 2019 EST</td></tr></table>"
       ],
       "text/plain": [
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@@ -746,7 +801,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.4"
+   "version": "3.7.5"
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diff --git a/qiskit/advanced/aqua/finance/optimization/portfolio_optimization.ipynb b/qiskit/advanced/aqua/finance/optimization/portfolio_optimization.ipynb
index 4f1ca589d..20d08671b 100644
--- a/qiskit/advanced/aqua/finance/optimization/portfolio_optimization.ipynb
+++ b/qiskit/advanced/aqua/finance/optimization/portfolio_optimization.ipynb
@@ -1,10 +1,15 @@
 {
  "cells": [
   {
+   "attachments": {
+    "qiskit_header.png": {
+     "image/png": 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"
+    }
+   },
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<img src=\"../../../../../images/qiskit_header.png\" alt=\"Note: In order for images to show up in this jupyter notebook you need to select File => Trusted Notebook\" align=\"middle\">"
+    "![qiskit_header.png](attachment:qiskit_header.png)"
    ]
   },
   {
@@ -13,15 +18,7 @@
     "collapsed": true
    },
    "source": [
-    "# _*Qiskit Finance: Portfolio Optimization*_ \n",
-    "\n",
-    "The latest version of this notebook is available on https://github.com/Qiskit/qiskit-iqx-tutorials.\n",
-    "\n",
-    "***\n",
-    "### Contributors\n",
-    "Stefan Woerner<sup>[1]</sup>, Daniel Egger<sup>[1]</sup>, Shaohan Hu<sup>[1]</sup>, Stephen Wood<sup>[1]</sup>, Marco Pistoia<sup>[1]</sup>\n",
-    "### Affliation\n",
-    "- <sup>[1]</sup>IBMQ"
+    "# Qiskit Finance: Portfolio Optimization"
    ]
   },
   {
@@ -60,11 +57,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
+   "execution_count": 15,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:32:27.923672Z",
+     "start_time": "2019-12-12T17:32:27.917959Z"
+    }
+   },
    "outputs": [],
    "source": [
-    "from qiskit import BasicAer\n",
+    "from qiskit import Aer\n",
     "from qiskit.aqua import QuantumInstance\n",
     "from qiskit.aqua import Operator, run_algorithm\n",
     "from qiskit.aqua.input import EnergyInput\n",
@@ -89,9 +91,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 16,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:32:36.256007Z",
+     "start_time": "2019-12-12T17:32:28.449895Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Credentials are already in use. The existing account in the session will be replaced.\n"
+     ]
+    }
+   ],
    "source": [
     "from qiskit import IBMQ\n",
     "provider = IBMQ.load_account()"
@@ -109,8 +124,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
+   "execution_count": 17,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:32:36.283153Z",
+     "start_time": "2019-12-12T17:32:36.273501Z"
+    }
+   },
    "outputs": [],
    "source": [
     "# set number of assets (= number of qubits)\n",
@@ -128,8 +148,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
+   "execution_count": 18,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:32:36.299281Z",
+     "start_time": "2019-12-12T17:32:36.293397Z"
+    }
+   },
    "outputs": [],
    "source": [
     "q = 0.5 # set risk factor\n",
@@ -149,8 +174,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
+   "execution_count": 19,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:32:36.319481Z",
+     "start_time": "2019-12-12T17:32:36.311062Z"
+    }
+   },
    "outputs": [],
    "source": [
     "def index_to_selection(i, num_assets):\n",
@@ -187,33 +217,38 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
+   "execution_count": 20,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:32:36.348016Z",
+     "start_time": "2019-12-12T17:32:36.333251Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Optimal: selection [1 1 0 0], value -0.0068\n",
+      "Optimal: selection [0 1 0 1], value -0.0088\n",
       "\n",
       "----------------- Full result ---------------------\n",
       "selection\tvalue\t\tprobability\n",
       "---------------------------------------------------\n",
-      " [1 1 0 0]\t-0.0068\t\t1.0000\n",
-      " [1 1 1 1]\t15.9945\t\t0.0000\n",
-      " [0 1 1 1]\t3.9954\t\t0.0000\n",
-      " [1 0 1 1]\t4.0000\t\t0.0000\n",
-      " [0 0 1 1]\t0.0010\t\t0.0000\n",
-      " [1 1 0 1]\t3.9926\t\t0.0000\n",
-      " [0 1 0 1]\t-0.0065\t\t0.0000\n",
-      " [1 0 0 1]\t-0.0017\t\t0.0000\n",
-      " [0 0 0 1]\t3.9993\t\t0.0000\n",
-      " [1 1 1 0]\t3.9951\t\t0.0000\n",
-      " [0 1 1 0]\t-0.0039\t\t0.0000\n",
-      " [1 0 1 0]\t0.0007\t\t0.0000\n",
-      " [0 0 1 0]\t4.0017\t\t0.0000\n",
-      " [0 1 0 0]\t3.9942\t\t0.0000\n",
-      " [1 0 0 0]\t3.9990\t\t0.0000\n",
+      " [0 1 0 1]\t-0.0088\t\t1.0000\n",
+      " [1 1 1 1]\t15.9908\t\t0.0000\n",
+      " [0 1 1 1]\t3.9906\t\t0.0000\n",
+      " [1 0 1 1]\t3.9967\t\t0.0000\n",
+      " [0 0 1 1]\t-0.0036\t\t0.0000\n",
+      " [1 1 0 1]\t3.9914\t\t0.0000\n",
+      " [1 0 0 1]\t-0.0026\t\t0.0000\n",
+      " [0 0 0 1]\t3.9971\t\t0.0000\n",
+      " [1 1 1 0]\t3.9938\t\t0.0000\n",
+      " [0 1 1 0]\t-0.0064\t\t0.0000\n",
+      " [1 0 1 0]\t-0.0005\t\t0.0000\n",
+      " [0 0 1 0]\t3.9993\t\t0.0000\n",
+      " [1 1 0 0]\t-0.0055\t\t0.0000\n",
+      " [0 1 0 0]\t3.9944\t\t0.0000\n",
+      " [1 0 0 0]\t4.0003\t\t0.0000\n",
       " [0 0 0 0]\t16.0000\t\t0.0000\n"
      ]
     }
@@ -249,39 +284,44 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
+   "execution_count": 21,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:32:52.517267Z",
+     "start_time": "2019-12-12T17:32:36.362801Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Optimal: selection [0 1 1 0], value -0.0039\n",
+      "Optimal: selection [0 1 1 0], value -0.0064\n",
       "\n",
       "----------------- Full result ---------------------\n",
       "selection\tvalue\t\tprobability\n",
       "---------------------------------------------------\n",
-      " [0 1 1 0]\t-0.0039\t\t0.8805\n",
-      " [0 0 1 1]\t0.0010\t\t0.1186\n",
-      " [1 1 0 0]\t-0.0068\t\t0.0008\n",
-      " [1 0 0 1]\t-0.0017\t\t0.0001\n",
-      " [1 0 1 0]\t0.0007\t\t0.0001\n",
-      " [0 1 0 1]\t-0.0065\t\t0.0000\n",
-      " [0 0 0 1]\t3.9993\t\t0.0000\n",
-      " [1 1 0 1]\t3.9926\t\t0.0000\n",
-      " [0 1 0 0]\t3.9942\t\t0.0000\n",
-      " [1 0 1 1]\t4.0000\t\t0.0000\n",
-      " [1 1 1 1]\t15.9945\t\t0.0000\n",
-      " [1 0 0 0]\t3.9990\t\t0.0000\n",
-      " [0 0 1 0]\t4.0017\t\t0.0000\n",
-      " [0 1 1 1]\t3.9954\t\t0.0000\n",
+      " [0 1 1 0]\t-0.0064\t\t0.6809\n",
+      " [0 0 1 1]\t-0.0036\t\t0.3086\n",
+      " [1 0 1 0]\t-0.0005\t\t0.0061\n",
+      " [1 1 0 0]\t-0.0055\t\t0.0027\n",
+      " [1 0 0 1]\t-0.0026\t\t0.0009\n",
+      " [0 1 0 1]\t-0.0088\t\t0.0004\n",
+      " [1 1 0 1]\t3.9914\t\t0.0002\n",
+      " [1 0 0 0]\t4.0003\t\t0.0001\n",
+      " [0 0 0 1]\t3.9971\t\t0.0000\n",
+      " [0 1 0 0]\t3.9944\t\t0.0000\n",
+      " [1 0 1 1]\t3.9967\t\t0.0000\n",
+      " [1 1 1 0]\t3.9938\t\t0.0000\n",
       " [0 0 0 0]\t16.0000\t\t0.0000\n",
-      " [1 1 1 0]\t3.9951\t\t0.0000\n"
+      " [0 0 1 0]\t3.9993\t\t0.0000\n",
+      " [1 1 1 1]\t15.9908\t\t0.0000\n",
+      " [0 1 1 1]\t3.9906\t\t0.0000\n"
      ]
     }
    ],
    "source": [
-    "backend = BasicAer.get_backend('statevector_simulator')\n",
+    "backend = Aer.get_backend('statevector_simulator')\n",
     "seed = 50\n",
     "\n",
     "cobyla = COBYLA()\n",
@@ -333,39 +373,44 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
+   "execution_count": 22,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:32:54.578898Z",
+     "start_time": "2019-12-12T17:32:52.527240Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Optimal: selection [0 0 1 1], value 0.0010\n",
+      "Optimal: selection [0 0 0 0], value 16.0000\n",
       "\n",
       "----------------- Full result ---------------------\n",
       "selection\tvalue\t\tprobability\n",
       "---------------------------------------------------\n",
-      " [0 0 1 1]\t0.0010\t\t0.1668\n",
-      " [1 0 1 0]\t0.0007\t\t0.1668\n",
-      " [1 0 0 1]\t-0.0017\t\t0.1667\n",
-      " [0 1 1 0]\t-0.0039\t\t0.1666\n",
-      " [0 1 0 1]\t-0.0065\t\t0.1665\n",
-      " [1 1 0 0]\t-0.0068\t\t0.1665\n",
-      " [0 0 1 0]\t4.0017\t\t0.0000\n",
-      " [1 0 1 1]\t4.0000\t\t0.0000\n",
-      " [1 1 1 1]\t15.9945\t\t0.0000\n",
-      " [0 0 0 1]\t3.9993\t\t0.0000\n",
-      " [1 0 0 0]\t3.9990\t\t0.0000\n",
-      " [0 1 0 0]\t3.9942\t\t0.0000\n",
-      " [1 1 0 1]\t3.9926\t\t0.0000\n",
-      " [0 1 1 1]\t3.9954\t\t0.0000\n",
-      " [1 1 1 0]\t3.9951\t\t0.0000\n",
-      " [0 0 0 0]\t16.0000\t\t0.0000\n"
+      " [0 0 0 1]\t3.9971\t\t0.0625\n",
+      " [0 0 1 0]\t3.9993\t\t0.0625\n",
+      " [0 1 0 0]\t3.9944\t\t0.0625\n",
+      " [1 0 0 0]\t4.0003\t\t0.0625\n",
+      " [0 0 0 0]\t16.0000\t\t0.0625\n",
+      " [0 0 1 1]\t-0.0036\t\t0.0625\n",
+      " [0 1 0 1]\t-0.0088\t\t0.0625\n",
+      " [1 0 0 1]\t-0.0026\t\t0.0625\n",
+      " [0 1 1 0]\t-0.0064\t\t0.0625\n",
+      " [1 0 1 0]\t-0.0005\t\t0.0625\n",
+      " [1 1 0 0]\t-0.0055\t\t0.0625\n",
+      " [0 1 1 1]\t3.9906\t\t0.0625\n",
+      " [1 0 1 1]\t3.9967\t\t0.0625\n",
+      " [1 1 0 1]\t3.9914\t\t0.0625\n",
+      " [1 1 1 0]\t3.9938\t\t0.0625\n",
+      " [1 1 1 1]\t15.9908\t\t0.0625\n"
      ]
     }
    ],
    "source": [
-    "backend = BasicAer.get_backend('statevector_simulator')\n",
+    "backend = Aer.get_backend('statevector_simulator')\n",
     "seed = 50\n",
     "\n",
     "cobyla = COBYLA()\n",
@@ -402,19 +447,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 23,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2019-08-22T01:54:45.323683Z",
-     "start_time": "2019-08-22T01:54:45.313579Z"
+     "end_time": "2019-12-12T17:32:54.597679Z",
+     "start_time": "2019-12-12T17:32:54.590297Z"
     }
    },
    "outputs": [
     {
      "data": {
       "text/html": [
-       "<h3>Version Information</h3><table><tr><th>Qiskit Software</th><th>Version</th></tr><tr><td>Qiskit</td><td>None</td></tr><tr><td>Terra</td><td>0.9.0</td></tr><tr><td>Aer</td><td>0.3.0</td></tr><tr><td>Ignis</td><td>0.2.0</td></tr><tr><td>Aqua</td><td>0.5.6</td></tr><tr><td>IBM Q Provider</td><td>0.3.2rc1</td></tr><tr><th>System information</th></tr><tr><td>Python</td><td>3.7.4 (default, Aug 13 2019, 15:17:50) \n",
-       "[Clang 4.0.1 (tags/RELEASE_401/final)]</td></tr><tr><td>OS</td><td>Darwin</td></tr><tr><td>CPUs</td><td>4</td></tr><tr><td>Memory (Gb)</td><td>16.0</td></tr><tr><td colspan='2'>Wed Aug 21 21:54:45 2019 EDT</td></tr></table>"
+       "<h3>Version Information</h3><table><tr><th>Qiskit Software</th><th>Version</th></tr><tr><td>Qiskit</td><td>0.14.0</td></tr><tr><td>Terra</td><td>0.11.0</td></tr><tr><td>Aer</td><td>0.3.4</td></tr><tr><td>Ignis</td><td>0.2.0</td></tr><tr><td>Aqua</td><td>0.6.1</td></tr><tr><td>IBM Q Provider</td><td>0.4.4</td></tr><tr><th>System information</th></tr><tr><td>Python</td><td>3.7.5 (default, Oct 25 2019, 10:52:18) \n",
+       "[Clang 4.0.1 (tags/RELEASE_401/final)]</td></tr><tr><td>OS</td><td>Darwin</td></tr><tr><td>CPUs</td><td>4</td></tr><tr><td>Memory (Gb)</td><td>16.0</td></tr><tr><td colspan='2'>Thu Dec 12 12:32:54 2019 EST</td></tr></table>"
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -466,7 +511,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.4"
+   "version": "3.7.5"
   },
   "varInspector": {
    "cols": {
diff --git a/qiskit/advanced/aqua/finance/simulation/.ipynb_checkpoints/asian_barrier_spread_pricing-checkpoint.ipynb b/qiskit/advanced/aqua/finance/simulation/.ipynb_checkpoints/asian_barrier_spread_pricing-checkpoint.ipynb
index 1ce496781..18c4953b8 100644
--- a/qiskit/advanced/aqua/finance/simulation/.ipynb_checkpoints/asian_barrier_spread_pricing-checkpoint.ipynb
+++ b/qiskit/advanced/aqua/finance/simulation/.ipynb_checkpoints/asian_barrier_spread_pricing-checkpoint.ipynb
@@ -1,25 +1,22 @@
 {
  "cells": [
   {
+   "attachments": {
+    "qiskit_header.png": {
+     "image/png": 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"
+    }
+   },
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<img src=\"../../../../../images/qiskit_header.png\" alt=\"Note: In order for images to show up in this jupyter notebook you need to select File => Trusted Notebook\" align=\"middle\">"
+    "![qiskit_header.png](attachment:qiskit_header.png)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# _*Qiskit Finance: Pricing Asian Barrier Spreads*_ \n",
-    "\n",
-    "The latest version of this notebook is available on https://github.com/Qiskit/qiskit-iqx-tutorials.\n",
-    "\n",
-    "***\n",
-    "### Contributors\n",
-    "Stefan Woerner<sup>[1]</sup>, Daniel Egger<sup>[1]</sup>\n",
-    "### Affliation\n",
-    "- <sup>[1]</sup>IBMQ"
+    "# Qiskit Finance: Pricing Asian Barrier Spreads"
    ]
   },
   {
@@ -60,8 +57,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
+   "execution_count": 15,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:45:49.593103Z",
+     "start_time": "2019-12-12T17:45:49.585361Z"
+    }
+   },
    "outputs": [],
    "source": [
     "import matplotlib.pyplot as plt\n",
@@ -70,7 +72,7 @@
     "%matplotlib inline\n",
     "import numpy as np\n",
     "\n",
-    "from qiskit import QuantumRegister, QuantumCircuit, BasicAer, execute\n",
+    "from qiskit import QuantumRegister, QuantumCircuit, Aer, execute\n",
     "from qiskit.aqua.algorithms import AmplitudeEstimation\n",
     "from qiskit.aqua.circuits import WeightedSumOperator, FixedValueComparator as Comparator\n",
     "from qiskit.aqua.components.uncertainty_problems import UnivariatePiecewiseLinearObjective as PwlObjective\n",
@@ -98,8 +100,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
+   "execution_count": 16,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:45:52.003138Z",
+     "start_time": "2019-12-12T17:45:51.989434Z"
+    }
+   },
    "outputs": [],
    "source": [
     "# number of qubits per dimension to represent the uncertainty \n",
@@ -137,8 +144,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
+   "execution_count": 17,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:45:54.248971Z",
+     "start_time": "2019-12-12T17:45:54.034528Z"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -211,8 +223,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
+   "execution_count": 18,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:45:56.620724Z",
+     "start_time": "2019-12-12T17:45:56.616363Z"
+    }
+   },
    "outputs": [],
    "source": [
     "# determine number of qubits required to represent total loss\n",
@@ -228,8 +245,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
+   "execution_count": 19,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:45:58.955640Z",
+     "start_time": "2019-12-12T17:45:58.947995Z"
+    }
+   },
    "outputs": [],
    "source": [
     "# set the strike price (should be within the low and the high value of the uncertainty)\n",
@@ -251,8 +273,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
+   "execution_count": 20,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:46:01.014063Z",
+     "start_time": "2019-12-12T17:46:01.009271Z"
+    }
+   },
    "outputs": [],
    "source": [
     "# condition and condition result\n",
@@ -265,8 +292,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
+   "execution_count": 21,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:46:03.264901Z",
+     "start_time": "2019-12-12T17:46:03.257104Z"
+    }
+   },
    "outputs": [],
    "source": [
     "# set the approximation scaling for the payoff function\n",
@@ -296,8 +328,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
+   "execution_count": 22,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:46:05.942721Z",
+     "start_time": "2019-12-12T17:46:05.485458Z"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -349,8 +386,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
+   "execution_count": 23,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:46:07.880221Z",
+     "start_time": "2019-12-12T17:46:07.872557Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -380,8 +422,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
+   "execution_count": 24,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:46:13.137268Z",
+     "start_time": "2019-12-12T17:46:12.829426Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -409,17 +456,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
+   "execution_count": 25,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:46:17.597554Z",
+     "start_time": "2019-12-12T17:46:16.757441Z"
+    }
+   },
    "outputs": [],
    "source": [
-    "job = execute(qc, backend=BasicAer.get_backend('statevector_simulator'))"
+    "job = execute(qc, backend=Aer.get_backend('statevector_simulator'))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
+   "execution_count": 26,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:46:20.539775Z",
+     "start_time": "2019-12-12T17:46:20.256459Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -460,8 +517,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
+   "execution_count": 27,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:46:22.567327Z",
+     "start_time": "2019-12-12T17:46:22.563234Z"
+    }
+   },
    "outputs": [],
    "source": [
     "# set number of evaluation qubits (=log(samples))\n",
@@ -473,8 +535,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
+   "execution_count": 28,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:56:19.718954Z",
+     "start_time": "2019-12-12T17:46:24.741251Z"
+    }
+   },
    "outputs": [],
    "source": [
     "# result = ae.run(quantum_instance=BasicAer.get_backend('qasm_simulator'), shots=100)\n",
@@ -483,8 +550,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
+   "execution_count": 29,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:56:22.568673Z",
+     "start_time": "2019-12-12T17:56:22.561336Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -492,7 +564,7 @@
      "text": [
       "Exact value:    \t0.8023\n",
       "Estimated value:\t0.5000\n",
-      "Probability:    \t0.6958\n"
+      "Probability:    \t0.2694\n"
      ]
     }
    ],
@@ -504,12 +576,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
+   "execution_count": 30,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:56:26.099389Z",
+     "start_time": "2019-12-12T17:56:25.788259Z"
+    }
+   },
    "outputs": [
     {
      "data": {
-      "image/png": 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9m/X19dHpfXa74VwnU8+5o6n1pu+/jsuebvy/6/LPTmxqf71gOJ8nzeq2Oml0oKUknS1pGfAW8IcKr2r6gVEV0kdSuWVTzU3Z+0dy26bC9jd8b3T7ZmbWgkbHwZwJnAP8kNQy+AfgYuA5YDnZpaYqlpL6Wt4haU9gWwb2ndQShffnSUFvXCHfOGB9VkYzM+uQRgPMF4ALgUuz77dGxEXAeFKA2LfG+nOBoyVtn0ubTHocQKN9PSdn708ARMRa0jidzxTyTQYe9R1kZmad1ehF3fcBiyLibUlvkV1+ioj1kq4GfkBq4QzmGlIr6BZJM4G9gRnA5flbl7NLcAsi4rTs+wxge9Igy5XAJ4CvA7dExM9z2/8WqX/mStI4nUnZ65gGj9PMzFrUaAvm98B22ecXgf+WWzaaNIhyUBHRDxxOGitzG3ARcAWpVZS3BRuPp1lKGidzHXAncCrw3ew9v/2HSC2bI4B5wKeAUz2K38ys8xptwTwMfIz0I/9j0gj8HYE/AV8G7q21gYhYDHyyRp6xhe9zSAMma4qIW0mtFzMzG0KNBpgZwB7Z50tIl8imklou84GvtqtgZmbW2xoKMBHxLPBs9nkt6VkwZ5VQLjMz63GtDLR8L7A78EpEvNy+IpmZ2XDQzCOTvyTpJeAF4CfAi5J+Lel/t710ZmbWsxodyX8BcBVpPMtxwITsfS7w/Wy5mZlZw5fIvgxcEhHfLKTflc0N9mXSyH4zM9vENXqJbASDP7VyARUmvzQzs01TowHmVuCkQZZ9Gri9teKYmdlwUc8jkyflvs4FLpU0loGPTB4P/H37i2hmZr2onj6Y2xn4aOQ9gKMr5P0X0pMmzcxsE1dPgHlf6aUwM7Nhp55HJr/QiYKYmdnw0vBIfklbkDr0DwF2BP4LeJA0dX5zDxw3M7Nhp6EAI2lX4G7gANITLF8DDiaNf3lK0lER8bt2F9LMzHpPo7cpXw7sBHw8IvaOiIMjYm/g41n65e0uoJmZ9aZGA8wk4OyI+Gk+Mft+LmnaGDMzs4YDzFbAHwZZ9gfgPa0Vx8zMhotGA8xjwNmSts0nZt/PzpabmZk1fBfZdOB+4CVJd5M6+XclDboUMLGtpTMzs57VUAsmIhYB+wKzgF2AI0kB5hpg34h4qu0lNDOznlR3C0bSlsCBwK8i4pzyimRmZsNBIy2Yt4H7gA+VVBYzMxtG6g4wEbEe+AUwprzimJnZcNHoXWTfAC6QtH8ZhTEzs+Gj0bvIzieN2F8k6WXSXWSRzxARB7apbGZm1sMaDTDPZC8zM7Oq6gowkkaQpol5BvgNcE9EvFZmwczMrLfV88jkvYF7gLG55JWSTomIu8sqmJmZ9bZ6OvkvBdYDfwVsA4wHngSuLbFcZmbW4+oJMAcD50fEwxHxZkQsAc4A/lzS7uUWz8zMelU9AWZ34JeFtOdJc4/t1vYSmZnZsFDvOJioncXMzOxd9d6mPE/Sugrp9xbTI2LX1otlZma9rp4Ac1HppTAzs2GnZoCJCAcYMzNrWKNzkZmZmdXFAcbMzErhAGNmZqVwgDEzs1I4wJiZWSkcYMzMrBQdDzCS9pN0r6TVkl6RdLGkzWus8zFJ10lalq33rKQLJW1dyDdDUlR4HVPuUZmZWVGjDxxriaTRpKn/FwPHA/sAl5EC3flVVp2c5Z0J/AI4APhW9v7pQt4VQDGgLGm17GZm1piOBhjgi8AI4KSIWAnMl7QDMEPSpVlaJTMj4ne5732S3gSulbRXRLyQW7YuIh4rp/hmZlavTl8iOxaYVwgkc0hB59DBVioElw2ezN4995mZWRfqdIAZByzNJ0TEi8DqbFkj/pL0ILRnC+mjJL0u6S1JT0o6qenSmplZ0xTRuZn4Jb0FfD0iriyk/xq4ISLOq3M7uwE/B+6MiKm59M+RWjSLgO1ID0abBHw6Im4ZZFvTgGkAY8aM+eicOXMaPayWrFq1iu22266j++x2w7lOnn55RVPrjRkBr61pfL399xjZ1P56wXA+T5o1FHVy2GGHPREREyotG4oA87WI+F4h/WXg+oj4Rh3beA/pRoH3Ah+NiP4qeQU8AoyIiA/X2vaECRNi4cKFtbK1VV9fHxMnTuzoPrvdcK6Tsefc0dR60/dfx2VPN95luvw7xzW1v14wnM+TZg1FnUgaNMB0+hJZPzCqQvpI4I1aK2cB4wZgPDCpWnABiBQ9bwEOqHUrtJmZtVen7yJbSqGvRdKewLYU+mYGcQXp9uYjI6Ke/Bv4iZxmZh3W6RbMXOBoSdvn0iYDa4AF1VaUdC7wVeBzEfFQPTvLWjwnAk9FxNvNFdnMzJrR6RbMNcCZwC2SZgJ7AzOAy/O3LktaBiyIiNOy76cClwDXAy9LOii3zec33MYsaQFwM6k1tC3wBeAg4IRyD8vMzIo6GmAiol/S4cBVwG2kfpcrSEGmWK58n8lR2fvU7JX3eVLgAVgG/C2wO+kW5p8Bx0XE3HaU38zM6tfpFgwRsRj4ZI08YwvfpzIwsFRa77QWimZmZm3k2ZTNzKwUDjBmZlYKBxgzMyuFA4yZmZXCAcbMzErR8bvIzMyGWrNzwjVrOM8JV41bMGZmVgoHGDMzK4UDjJmZlcIBxszMSuEAY2ZmpXCAMTOzUjjAmJlZKRxgzMysFA4wZmZWCgcYMzMrhQOMmZmVwgHGzMxK4QBjZmalcIAxM7NSOMCYmVkpHGDMzKwUDjBmZlYKBxgzMyuFA4yZmZXCAcbMzErhAGNmZqVwgDEzs1I4wJiZWSkcYMzMrBQOMGZmVgoHGDMzK4UDjJmZlcIBxszMSrHFUBfAOmPsOXd0dH/Lv3NcR/dnZt3HLRgzMyuFA4yZmZXCAcbMzErhAGNmZqVwgDEzs1L4LjIzsy7T7F2f0/dfx9Qm1i3rrk+3YMzMrBQdDzCS9pN0r6TVkl6RdLGkzetYb6Sk6yT1S1oh6UeSdqqQ73hJT0t6U9JiSZPLORIzM6umo5fIJI0G7gEWA8cD+wCXkQLd+TVWvxH4IHA6sB6YCdwK/FVu+4cANwNXA2cCk4DZkvoj4u62HkxBJ5u0HsRoZr2g030wXwRGACdFxEpgvqQdgBmSLs3SBpB0MHA0cGhEPJClvQz8RNIREXFPlvWbwAMRcWb2/X5J44ELgFIDjJmZbazTl8iOBeYVAskcUtA5tMZ6r20ILgAR8Tjwq2wZkrYCDgP+tbDuHOBgSSNbL76ZmdWr0wFmHLA0nxARLwKrs2V1r5dZkltvH2DLCvmWkI7zA02U18zMmtTpS2SjgTcqpPdny5pZb+9cHirk6y8s34ikacC07OsqSc9WKUfbnQk7A683so5mllSYNmqxjA3XyXDXzHkCvXGutKBnzpNO/TsM0Xmy12ALhmIcTFRI0yDpzaxX/K4q6xMRs4BZNfZdGkkLI2LCUO2/G7lOBnKdDOQ6Gajb6qTTl8j6gVEV0kdSuYVSa71RufX6c2nFPNTYvpmZtVmnA8xSCn0tkvYEtqVyH8ug62XyfTPPA29VyDeOdFvzc02U18zMmtTpADMXOFrS9rm0ycAaYEGN9XbLxrkAIGkCqf9lLkBErAXuBz5TWHcy8GhErGi9+KUYsstzXcx1MpDrZCDXyUBdVSeKqNX10cadpYGWi4FnSAMl9wYuB66MiPNz+ZYBCyLitFzaXaQ7wb7GuwMtfxsRxYGWfcBVpEGYk7L8x5Q90NLMzDbW0RZMRPQDhwObA7cBFwFXABcWsm6R5cmbQmrl/DNwA/AEcGJh+w8BJwNHAPOATwGnOriYmXVeR1swZma26fBsym3gCTwHaqZOJH0sq49l2XrPSrpQ0taFfDMkRYXXMeUeVWuarJOxgxzrnAp5e+48gabrZbBzICSdm8t3/SB5qg3sHlKS3i/pWklPSXpbUl+d63Xd74mfB9Oi4TyBZ7NaqJPJWd6ZwC+AA4BvZe+fLuRdARQDypJWy16WFs8TSH2JD+e+bzSYrhfPE2ipXn4A3FVIOwE4m+zGn5ylwOcLacubK3FHjCf9+z0GvKeB9brv9yQi/GrhBZxLGoOzQy7t70nT3+xQZb2DSYM/P5FLOzBLOyKXNg+4r7DuncBDQ33sJdTJLhXSpmV1slcubQbw+lAfZ4fqZGx2/P+jxvZ77jxppV4G2dYdwJJC2vXAwqE+zgaPY7Pc55uAvjrW6crfE18ia50n8ByoqTqJiN9VSH4ye9+1fcUbEs2eJzX18HkCbaoXSTsCRwKz21u8zouI9U2s1pW/Jw4wrfMEngM1WyeV/CWpuV+cI26UpNclvSXpSUknNV3azmi1Tq7Lrse/KulySSNyy3r1PIH2nSsnk+pgQN8UsJ+klZLWSnpIUksBvUt15e+JA0zrypjAc3QuDxXyVZ3Asws0WycbkbQb8A3g/xb+wl1GuoxyCqlv5hXg5i4PMs3WyVrgn4DTSLf4Xwt8iY1/SHv1PIE2nSukYQw/i4jijB1PAtOB/wl8ljT8Yb6kA5soazfryt8Td/K3R1dN4Nklmq2TlFF6D6kpvwr4u402HPEvhby3AY+QHix3SzOF7ZCG6yQiXgW+kkvqk/QacLWkD0fEoirb74XzBFo/V3YnXU47e8CGI75XyHsH6YaC80g3BQwnXfd74hZM6zyB50DN1gkAkkQaTDsemBRpgO6gIvVU3gIcUM/t4UOkpTopuCl7/0hu21TYfrefJ9CeejmF9CN5Y62MEbGG1Kn9kVp5e0xX/p44wLTOE3gO1GydbHAF6ZbV4yOinvwbdPNf6q3WSV4U3nv1PIH21MsU0l1QLzWw324+V5rRlb8nDjCt8wSeAzVbJ2SD5L4KfC7S1D81ZS2eE4GnIuLt5opcuqbrpIKTs/cnoKfPE2ixXiSNBQ6izrvHspsjjiWru2GkO39Phvqe715/kTrGXgXmk+ZAm0bqN/h2Id8y4IeFtLuAXwInka4HPws8WMhzCLAOuBKYCFxK+mvjqKE+9nbXCXAq6S/L60g/GvnXLrl8C0iDxI4iBZY7szr51FAfewl1MoM08PCkbL2LST++N/f6edJKveTSzyH9VV5pDNVI4EHgDNINEpNJgxfXAhOG+tir1Mk2pD8iTgYeBf4z932bweqjG39Phrwyh8ML2A+4L/uP/ypp9PnmhTzLgesLaaOyH9M3gJXAj4GdK2z/BNIM1GtJzd0pQ33MZdQJaVBcDPKamsv3w+w/0hrgj9mPyLFDfcwl1ckUYCFp5oI/ZT8sFwNbDYfzpNl6yaUvAu4aZLtbk/rmXsrqZEX2I3zQUB9zjfoYW+X/wdjB6qMbf0882aWZmZXCfTBmZlYKBxgzMyuFA4yZmZXCAcbMzErhAGNmZqVwgDEzs1I4wJiZWSkcYMzMrBT/H5ECYukA/OkPAAAAAElFTkSuQmCC\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -521,7 +598,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -558,19 +635,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 31,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2019-08-22T01:55:10.230176Z",
-     "start_time": "2019-08-22T01:55:10.222004Z"
+     "end_time": "2019-12-12T17:56:29.135968Z",
+     "start_time": "2019-12-12T17:56:29.021462Z"
     }
    },
    "outputs": [
     {
      "data": {
       "text/html": [
-       "<h3>Version Information</h3><table><tr><th>Qiskit Software</th><th>Version</th></tr><tr><td>Qiskit</td><td>None</td></tr><tr><td>Terra</td><td>0.9.0</td></tr><tr><td>Aer</td><td>0.3.0</td></tr><tr><td>Ignis</td><td>0.2.0</td></tr><tr><td>Aqua</td><td>0.5.6</td></tr><tr><td>IBM Q Provider</td><td>0.3.2rc1</td></tr><tr><th>System information</th></tr><tr><td>Python</td><td>3.7.4 (default, Aug 13 2019, 15:17:50) \n",
-       "[Clang 4.0.1 (tags/RELEASE_401/final)]</td></tr><tr><td>OS</td><td>Darwin</td></tr><tr><td>CPUs</td><td>4</td></tr><tr><td>Memory (Gb)</td><td>16.0</td></tr><tr><td colspan='2'>Wed Aug 21 21:55:10 2019 EDT</td></tr></table>"
+       "<h3>Version Information</h3><table><tr><th>Qiskit Software</th><th>Version</th></tr><tr><td>Qiskit</td><td>0.14.0</td></tr><tr><td>Terra</td><td>0.11.0</td></tr><tr><td>Aer</td><td>0.3.4</td></tr><tr><td>Ignis</td><td>0.2.0</td></tr><tr><td>Aqua</td><td>0.6.1</td></tr><tr><td>IBM Q Provider</td><td>0.4.4</td></tr><tr><th>System information</th></tr><tr><td>Python</td><td>3.7.5 (default, Oct 25 2019, 10:52:18) \n",
+       "[Clang 4.0.1 (tags/RELEASE_401/final)]</td></tr><tr><td>OS</td><td>Darwin</td></tr><tr><td>CPUs</td><td>4</td></tr><tr><td>Memory (Gb)</td><td>16.0</td></tr><tr><td colspan='2'>Thu Dec 12 12:56:29 2019 EST</td></tr></table>"
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -622,7 +699,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.4"
+   "version": "3.7.5"
   },
   "varInspector": {
    "cols": {
diff --git a/qiskit/advanced/aqua/finance/simulation/.ipynb_checkpoints/basket_option_pricing-checkpoint.ipynb b/qiskit/advanced/aqua/finance/simulation/.ipynb_checkpoints/basket_option_pricing-checkpoint.ipynb
index 1b62851eb..5d9a187b4 100644
--- a/qiskit/advanced/aqua/finance/simulation/.ipynb_checkpoints/basket_option_pricing-checkpoint.ipynb
+++ b/qiskit/advanced/aqua/finance/simulation/.ipynb_checkpoints/basket_option_pricing-checkpoint.ipynb
@@ -1,25 +1,22 @@
 {
  "cells": [
   {
+   "attachments": {
+    "qiskit_header.png": {
+     "image/png": 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"
+    }
+   },
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<img src=\"../../../../../images/qiskit_header.png\" alt=\"Note: In order for images to show up in this jupyter notebook you need to select File => Trusted Notebook\" align=\"middle\">"
+    "![qiskit_header.png](attachment:qiskit_header.png)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# _*Qiskit Finance: Pricing Basket Options*_ \n",
-    "\n",
-    "The latest version of this notebook is available on https://github.com/Qiskit/qiskit-iqx-tutorials.\n",
-    "\n",
-    "***\n",
-    "### Contributors\n",
-    "Stefan Woerner<sup>[1]</sup>, Daniel Egger<sup>[1]</sup>\n",
-    "### Affliation\n",
-    "- <sup>[1]</sup>IBMQ"
+    "# Qiskit Finance: Pricing Basket Options"
    ]
   },
   {
@@ -47,8 +44,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
+   "execution_count": 2,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:48:15.091384Z",
+     "start_time": "2019-12-12T17:48:15.074739Z"
+    }
+   },
    "outputs": [],
    "source": [
     "import matplotlib.pyplot as plt\n",
@@ -57,7 +59,7 @@
     "%matplotlib inline\n",
     "import numpy as np\n",
     "\n",
-    "from qiskit import BasicAer, QuantumRegister, QuantumCircuit, execute\n",
+    "from qiskit import Aer, QuantumRegister, QuantumCircuit, execute\n",
     "from qiskit.aqua.algorithms import AmplitudeEstimation\n",
     "from qiskit.aqua.components.uncertainty_problems import MultivariateProblem\n",
     "from qiskit.aqua.components.uncertainty_problems import UnivariatePiecewiseLinearObjective as PwlObjective\n",
@@ -85,8 +87,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
+   "execution_count": 3,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:48:16.343149Z",
+     "start_time": "2019-12-12T17:48:16.322565Z"
+    }
+   },
    "outputs": [],
    "source": [
     "# number of qubits per dimension to represent the uncertainty \n",
@@ -124,8 +131,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
+   "execution_count": 4,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:48:17.743717Z",
+     "start_time": "2019-12-12T17:48:17.158077Z"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -192,8 +204,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
+   "execution_count": 5,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:48:18.274595Z",
+     "start_time": "2019-12-12T17:48:18.267991Z"
+    }
+   },
    "outputs": [],
    "source": [
     "# determine number of qubits required to represent total loss\n",
@@ -209,8 +226,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
+   "execution_count": 6,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:48:18.946439Z",
+     "start_time": "2019-12-12T17:48:18.935294Z"
+    }
+   },
    "outputs": [],
    "source": [
     "# set the strike price (should be within the low and the high value of the uncertainty)\n",
@@ -249,8 +271,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
+   "execution_count": 7,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:48:20.314739Z",
+     "start_time": "2019-12-12T17:48:20.100968Z"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -281,8 +308,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
+   "execution_count": 8,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:48:20.914716Z",
+     "start_time": "2019-12-12T17:48:20.908251Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -310,8 +342,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
+   "execution_count": 10,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:48:31.104508Z",
+     "start_time": "2019-12-12T17:48:30.970506Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -339,17 +376,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
+   "execution_count": 11,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:48:32.350280Z",
+     "start_time": "2019-12-12T17:48:32.042056Z"
+    }
+   },
    "outputs": [],
    "source": [
-    "job = execute(qc, backend=BasicAer.get_backend('statevector_simulator'))"
+    "job = execute(qc, backend=Aer.get_backend('statevector_simulator'))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
+   "execution_count": 12,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:48:33.425816Z",
+     "start_time": "2019-12-12T17:48:33.403644Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -386,8 +433,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
+   "execution_count": 13,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:48:34.420915Z",
+     "start_time": "2019-12-12T17:48:34.415416Z"
+    }
+   },
    "outputs": [],
    "source": [
     "# set number of evaluation qubits (=log(samples))\n",
@@ -399,8 +451,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 14,
    "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:51:40.049915Z",
+     "start_time": "2019-12-12T17:48:35.268300Z"
+    },
     "scrolled": true
    },
    "outputs": [],
@@ -411,8 +467,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
+   "execution_count": 15,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:51:41.113659Z",
+     "start_time": "2019-12-12T17:51:41.102390Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -432,8 +493,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
+   "execution_count": 16,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:51:42.566087Z",
+     "start_time": "2019-12-12T17:51:42.156782Z"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -486,19 +552,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 17,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2019-08-22T01:55:31.656034Z",
-     "start_time": "2019-08-22T01:55:31.646368Z"
+     "end_time": "2019-12-12T17:51:43.795082Z",
+     "start_time": "2019-12-12T17:51:43.656955Z"
     }
    },
    "outputs": [
     {
      "data": {
       "text/html": [
-       "<h3>Version Information</h3><table><tr><th>Qiskit Software</th><th>Version</th></tr><tr><td>Qiskit</td><td>None</td></tr><tr><td>Terra</td><td>0.9.0</td></tr><tr><td>Aer</td><td>0.3.0</td></tr><tr><td>Ignis</td><td>0.2.0</td></tr><tr><td>Aqua</td><td>0.5.6</td></tr><tr><td>IBM Q Provider</td><td>0.3.2rc1</td></tr><tr><th>System information</th></tr><tr><td>Python</td><td>3.7.4 (default, Aug 13 2019, 15:17:50) \n",
-       "[Clang 4.0.1 (tags/RELEASE_401/final)]</td></tr><tr><td>OS</td><td>Darwin</td></tr><tr><td>CPUs</td><td>4</td></tr><tr><td>Memory (Gb)</td><td>16.0</td></tr><tr><td colspan='2'>Wed Aug 21 21:55:31 2019 EDT</td></tr></table>"
+       "<h3>Version Information</h3><table><tr><th>Qiskit Software</th><th>Version</th></tr><tr><td>Qiskit</td><td>0.14.0</td></tr><tr><td>Terra</td><td>0.11.0</td></tr><tr><td>Aer</td><td>0.3.4</td></tr><tr><td>Ignis</td><td>0.2.0</td></tr><tr><td>Aqua</td><td>0.6.1</td></tr><tr><td>IBM Q Provider</td><td>0.4.4</td></tr><tr><th>System information</th></tr><tr><td>Python</td><td>3.7.5 (default, Oct 25 2019, 10:52:18) \n",
+       "[Clang 4.0.1 (tags/RELEASE_401/final)]</td></tr><tr><td>OS</td><td>Darwin</td></tr><tr><td>CPUs</td><td>4</td></tr><tr><td>Memory (Gb)</td><td>16.0</td></tr><tr><td colspan='2'>Thu Dec 12 12:51:43 2019 EST</td></tr></table>"
       ],
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@@ -550,7 +616,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.4"
+   "version": "3.7.5"
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diff --git a/qiskit/advanced/aqua/finance/simulation/.ipynb_checkpoints/bull_spread_pricing-checkpoint.ipynb b/qiskit/advanced/aqua/finance/simulation/.ipynb_checkpoints/bull_spread_pricing-checkpoint.ipynb
index fa23b5eef..5b14e33dc 100644
--- a/qiskit/advanced/aqua/finance/simulation/.ipynb_checkpoints/bull_spread_pricing-checkpoint.ipynb
+++ b/qiskit/advanced/aqua/finance/simulation/.ipynb_checkpoints/bull_spread_pricing-checkpoint.ipynb
@@ -1,25 +1,22 @@
 {
  "cells": [
   {
+   "attachments": {
+    "qiskit_header.png": {
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"
+    }
+   },
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<img src=\"../../../../../images/qiskit_header.png\" alt=\"Note: In order for images to show up in this jupyter notebook you need to select File => Trusted Notebook\" align=\"middle\">"
+    "![qiskit_header.png](attachment:qiskit_header.png)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# _*Qiskit Finance: Pricing Bull Spreads*_ \n",
-    "\n",
-    "The latest version of this notebook is available on https://github.com/Qiskit/qiskit-iqx-tutorials.\n",
-    "\n",
-    "***\n",
-    "### Contributors\n",
-    "Stefan Woerner<sup>[1]</sup>, Daniel Egger<sup>[1]</sup>\n",
-    "### Affliation\n",
-    "- <sup>[1]</sup>IBMQ"
+    "# Qiskit Finance: Pricing Bull Spreads"
    ]
   },
   {
@@ -55,14 +52,19 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:50:29.482202Z",
+     "start_time": "2019-12-12T17:50:25.441150Z"
+    }
+   },
    "outputs": [],
    "source": [
     "import matplotlib.pyplot as plt\n",
     "%matplotlib inline\n",
     "import numpy as np\n",
     "\n",
-    "from qiskit import BasicAer\n",
+    "from qiskit import Aer\n",
     "from qiskit.aqua.algorithms import AmplitudeEstimation\n",
     "from qiskit.aqua.components.uncertainty_models import LogNormalDistribution\n",
     "from qiskit.aqua.components.uncertainty_problems import UnivariateProblem\n",
@@ -86,7 +88,12 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:50:29.540739Z",
+     "start_time": "2019-12-12T17:50:29.525604Z"
+    }
+   },
    "outputs": [],
    "source": [
     "# number of qubits to represent the uncertainty\n",
@@ -116,7 +123,12 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:50:30.293077Z",
+     "start_time": "2019-12-12T17:50:29.586659Z"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -175,7 +187,12 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:50:30.356784Z",
+     "start_time": "2019-12-12T17:50:30.340964Z"
+    }
+   },
    "outputs": [],
    "source": [
     "# set the strike price (should be within the low and the high value of the uncertainty)\n",
@@ -213,7 +230,12 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:50:30.669532Z",
+     "start_time": "2019-12-12T17:50:30.390823Z"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -245,7 +267,12 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:50:30.752831Z",
+     "start_time": "2019-12-12T17:50:30.742849Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -274,7 +301,12 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:50:30.810295Z",
+     "start_time": "2019-12-12T17:50:30.802085Z"
+    }
+   },
    "outputs": [],
    "source": [
     "# set number of evaluation qubits (=log(samples))\n",
@@ -286,18 +318,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
+   "execution_count": 9,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:51:27.569545Z",
+     "start_time": "2019-12-12T17:50:33.522717Z"
+    }
+   },
    "outputs": [],
    "source": [
     "# result = ae.run(quantum_instance=BasicAer.get_backend('qasm_simulator'), shots=100)\n",
-    "result = ae.run(quantum_instance=BasicAer.get_backend('statevector_simulator'))"
+    "result = ae.run(quantum_instance=Aer.get_backend('statevector_simulator'))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
+   "execution_count": 10,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:51:27.620857Z",
+     "start_time": "2019-12-12T17:51:27.615249Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -317,8 +359,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
+   "execution_count": 11,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:51:28.158545Z",
+     "start_time": "2019-12-12T17:51:27.653954Z"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -381,8 +428,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
+   "execution_count": 12,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:51:28.210508Z",
+     "start_time": "2019-12-12T17:51:28.202207Z"
+    }
+   },
    "outputs": [],
    "source": [
     "# setup piecewise linear objective fcuntion\n",
@@ -413,8 +465,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
+   "execution_count": 13,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:51:28.255341Z",
+     "start_time": "2019-12-12T17:51:28.246180Z"
+    }
+   },
    "outputs": [],
    "source": [
     "# set number of evaluation qubits (=log(samples))\n",
@@ -426,18 +483,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
+   "execution_count": 15,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:52:26.499223Z",
+     "start_time": "2019-12-12T17:51:37.289794Z"
+    }
+   },
    "outputs": [],
    "source": [
     "# result_delta = ae_delta.run(quantum_instance=BasicAer.get_backend('qasm_simulator'), shots=100)\n",
-    "result_delta = ae_delta.run(quantum_instance=BasicAer.get_backend('statevector_simulator'))"
+    "result_delta = ae_delta.run(quantum_instance=Aer.get_backend('statevector_simulator'))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
+   "execution_count": 16,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:52:26.538733Z",
+     "start_time": "2019-12-12T17:52:26.532073Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -457,8 +524,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
+   "execution_count": 17,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:52:26.843582Z",
+     "start_time": "2019-12-12T17:52:26.569419Z"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -488,19 +560,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 18,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2019-08-22T01:55:52.763931Z",
-     "start_time": "2019-08-22T01:55:52.753702Z"
+     "end_time": "2019-12-12T17:52:27.016687Z",
+     "start_time": "2019-12-12T17:52:26.891140Z"
     }
    },
    "outputs": [
     {
      "data": {
       "text/html": [
-       "<h3>Version Information</h3><table><tr><th>Qiskit Software</th><th>Version</th></tr><tr><td>Qiskit</td><td>None</td></tr><tr><td>Terra</td><td>0.9.0</td></tr><tr><td>Aer</td><td>0.3.0</td></tr><tr><td>Ignis</td><td>0.2.0</td></tr><tr><td>Aqua</td><td>0.5.6</td></tr><tr><td>IBM Q Provider</td><td>0.3.2rc1</td></tr><tr><th>System information</th></tr><tr><td>Python</td><td>3.7.4 (default, Aug 13 2019, 15:17:50) \n",
-       "[Clang 4.0.1 (tags/RELEASE_401/final)]</td></tr><tr><td>OS</td><td>Darwin</td></tr><tr><td>CPUs</td><td>4</td></tr><tr><td>Memory (Gb)</td><td>16.0</td></tr><tr><td colspan='2'>Wed Aug 21 21:55:52 2019 EDT</td></tr></table>"
+       "<h3>Version Information</h3><table><tr><th>Qiskit Software</th><th>Version</th></tr><tr><td>Qiskit</td><td>0.14.0</td></tr><tr><td>Terra</td><td>0.11.0</td></tr><tr><td>Aer</td><td>0.3.4</td></tr><tr><td>Ignis</td><td>0.2.0</td></tr><tr><td>Aqua</td><td>0.6.1</td></tr><tr><td>IBM Q Provider</td><td>0.4.4</td></tr><tr><th>System information</th></tr><tr><td>Python</td><td>3.7.5 (default, Oct 25 2019, 10:52:18) \n",
+       "[Clang 4.0.1 (tags/RELEASE_401/final)]</td></tr><tr><td>OS</td><td>Darwin</td></tr><tr><td>CPUs</td><td>4</td></tr><tr><td>Memory (Gb)</td><td>16.0</td></tr><tr><td colspan='2'>Thu Dec 12 12:52:27 2019 EST</td></tr></table>"
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -552,7 +624,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.4"
+   "version": "3.7.5"
   },
   "varInspector": {
    "cols": {
diff --git a/qiskit/advanced/aqua/finance/simulation/.ipynb_checkpoints/credit_risk_analysis-checkpoint.ipynb b/qiskit/advanced/aqua/finance/simulation/.ipynb_checkpoints/credit_risk_analysis-checkpoint.ipynb
index 892fe45e9..8fc3e4d7d 100644
--- a/qiskit/advanced/aqua/finance/simulation/.ipynb_checkpoints/credit_risk_analysis-checkpoint.ipynb
+++ b/qiskit/advanced/aqua/finance/simulation/.ipynb_checkpoints/credit_risk_analysis-checkpoint.ipynb
@@ -1,26 +1,22 @@
 {
  "cells": [
   {
+   "attachments": {
+    "qiskit_header.png": {
+     "image/png": 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"
+    }
+   },
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<img src=\"../../../../../images/qiskit_header.png\" alt=\"Note: In order for images to show up in this jupyter notebook you need to select File => Trusted Notebook\" align=\"middle\">"
+    "![qiskit_header.png](attachment:qiskit_header.png)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# _*Qiskit Finance: Credit Risk Analysis*_ \n",
-    "\n",
-    "The latest version of this notebook is available on https://github.com/Qiskit/qiskit-iqx-tutorials.\n",
-    "\n",
-    "***\n",
-    "### Contributors\n",
-    "Stefan Woerner<sup>[1]</sup>, Daniel Egger<sup>[1]</sup>\n",
-    "\n",
-    "### Affliation\n",
-    "- <sup>[1]</sup>IBMQ"
+    "# Qiskit Finance: Credit Risk Analysis"
    ]
   },
   {
@@ -49,11 +45,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
+   "execution_count": 4,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:52:26.773593Z",
+     "start_time": "2019-12-12T17:52:26.766665Z"
+    }
+   },
    "outputs": [],
    "source": [
-    "from qiskit import QuantumRegister, QuantumCircuit, BasicAer, execute\n",
+    "from qiskit import QuantumRegister, QuantumCircuit, Aer, execute\n",
     "\n",
     "from qiskit.aqua.components.uncertainty_models import GaussianConditionalIndependenceModel as GCI\n",
     "from qiskit.aqua.components.uncertainty_problems import UnivariatePiecewiseLinearObjective as PwlObjective\n",
@@ -68,12 +69,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
+   "execution_count": 6,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:52:31.609401Z",
+     "start_time": "2019-12-12T17:52:31.602810Z"
+    }
+   },
    "outputs": [],
    "source": [
     "# define backend to be used\n",
-    "backend = BasicAer.get_backend('statevector_simulator')"
+    "backend = Aer.get_backend('statevector_simulator')"
    ]
   },
   {
@@ -117,8 +123,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
+   "execution_count": 7,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:52:32.252197Z",
+     "start_time": "2019-12-12T17:52:32.246744Z"
+    }
+   },
    "outputs": [],
    "source": [
     "# set problem parameters\n",
@@ -148,8 +159,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
+   "execution_count": 8,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:52:32.667519Z",
+     "start_time": "2019-12-12T17:52:32.658824Z"
+    }
+   },
    "outputs": [],
    "source": [
     "# construct circuit factory for uncertainty model (Gaussian Conditional Independence model)\n",
@@ -158,8 +174,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
+   "execution_count": 9,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:52:32.863444Z",
+     "start_time": "2019-12-12T17:52:32.836298Z"
+    }
+   },
    "outputs": [],
    "source": [
     "# determine the number of qubits required to represent the uncertainty model\n",
@@ -186,8 +207,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
+   "execution_count": 10,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:52:33.224440Z",
+     "start_time": "2019-12-12T17:52:33.169709Z"
+    }
+   },
    "outputs": [],
    "source": [
     "# run the circuit and analyze the results\n",
@@ -196,8 +222,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
+   "execution_count": 11,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:52:33.358445Z",
+     "start_time": "2019-12-12T17:52:33.339867Z"
+    }
+   },
    "outputs": [],
    "source": [
     "# analyze uncertainty circuit and determine exact solutions\n",
@@ -242,8 +273,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
+   "execution_count": 12,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:52:34.310896Z",
+     "start_time": "2019-12-12T17:52:33.500251Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -356,8 +392,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
+   "execution_count": 13,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:52:34.398210Z",
+     "start_time": "2019-12-12T17:52:34.390573Z"
+    }
+   },
    "outputs": [],
    "source": [
     "# determine number of qubits required to represent total loss\n",
@@ -369,8 +410,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
+   "execution_count": 14,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:52:34.495588Z",
+     "start_time": "2019-12-12T17:52:34.483985Z"
+    }
+   },
    "outputs": [],
    "source": [
     "# define linear objective function\n",
@@ -406,8 +452,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
+   "execution_count": 15,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:52:34.621639Z",
+     "start_time": "2019-12-12T17:52:34.592667Z"
+    }
+   },
    "outputs": [],
    "source": [
     "num_qubits = multivariate.num_target_qubits\n",
@@ -422,8 +473,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 16,
    "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:52:34.840139Z",
+     "start_time": "2019-12-12T17:52:34.779119Z"
+    },
     "scrolled": true
    },
    "outputs": [
@@ -464,97 +519,97 @@
        "«                                                                           »\n",
        "«q_a_2: ────────────────────────────────────────────────────────────────────»\n",
        "«                                                                           »\n",
-       "«                                                             »\n",
-       "«  q_0: ──■────■───────────────────────────────────────────■──»\n",
-       "«         │    │                                           │  »\n",
-       "«  q_1: ──┼────┼───────────────────────────────■───────────┼──»\n",
-       "«       ┌─┴─┐  │  ┌──────────────────┐       ┌─┴─┐         │  »\n",
-       "«  q_2: ┤ X ├──┼──┤ U3(-0.28365,0,0) ├───────┤ X ├─────────┼──»\n",
-       "«       └───┘┌─┴─┐└──────────────────┘┌──────┴───┴──────┐┌─┴─┐»\n",
-       "«  q_3: ─────┤ X ├────────────────────┤ U3(0.11174,0,0) ├┤ X ├»\n",
-       "«            └───┘                    └─────────────────┘└───┘»\n",
-       "«  q_4: ──────────────────────────────────────────────────────»\n",
-       "«                                                             »\n",
-       "«q_a_0: ──────────────────────────────────────────────────────»\n",
-       "«                                                             »\n",
-       "«q_a_1: ──────────────────────────────────────────────────────»\n",
-       "«                                                             »\n",
-       "«q_a_2: ──────────────────────────────────────────────────────»\n",
-       "«                                                             »\n",
+       "«                                                                           »\n",
+       "«  q_0: ──■────────────────────────■────────────────────────────────────────»\n",
+       "«         │                        │                                        »\n",
+       "«  q_1: ──┼────────────────────────┼───────────■────────────────────────────»\n",
+       "«       ┌─┴─┐┌──────────────────┐  │         ┌─┴─┐       ┌─────────────────┐»\n",
+       "«  q_2: ┤ X ├┤ U3(-0.28365,0,0) ├──┼─────────┤ X ├───────┤ U3(0.28365,0,0) ├»\n",
+       "«       └───┘└──────────────────┘┌─┴─┐┌──────┴───┴──────┐└─────────────────┘»\n",
+       "«  q_3: ─────────────────────────┤ X ├┤ U3(0.11174,0,0) ├───────────────────»\n",
+       "«                                └───┘└─────────────────┘                   »\n",
+       "«  q_4: ────────────────────────────────────────────────────────────────────»\n",
+       "«                                                                           »\n",
+       "«q_a_0: ────────────────────────────────────────────────────────────────────»\n",
+       "«                                                                           »\n",
+       "«q_a_1: ────────────────────────────────────────────────────────────────────»\n",
+       "«                                                                           »\n",
+       "«q_a_2: ────────────────────────────────────────────────────────────────────»\n",
+       "«                                                                           »\n",
+       "«                                                              ░           »\n",
+       "«  q_0: ──■────────────────────────────────────────────────────░───────────»\n",
+       "«         │                                                    ░           »\n",
+       "«  q_1: ──┼───────────■────────────■───────────────────────■───░───────────»\n",
+       "«         │         ┌─┴─┐          │                       │   ░           »\n",
+       "«  q_2: ──┼─────────┤ X ├──────────┼───────────────────────┼───░───■────■──»\n",
+       "«       ┌─┴─┐┌──────┴───┴───────┐┌─┴─┐┌─────────────────┐┌─┴─┐ ░   │    │  »\n",
+       "«  q_3: ┤ X ├┤ U3(-0.22349,0,0) ├┤ X ├┤ U3(0.22349,0,0) ├┤ X ├─░───┼────┼──»\n",
+       "«       └───┘└──────────────────┘└───┘└─────────────────┘└───┘ ░   │    │  »\n",
+       "«  q_4: ───────────────────────────────────────────────────────░───┼────┼──»\n",
+       "«                                                              ░   │  ┌─┴─┐»\n",
+       "«q_a_0: ───────────────────────────────────────────────────────░───■──┤ X ├»\n",
+       "«                                                              ░   │  └───┘»\n",
+       "«q_a_1: ───────────────────────────────────────────────────────░───┼───────»\n",
+       "«                                                              ░ ┌─┴─┐     »\n",
+       "«q_a_2: ───────────────────────────────────────────────────────░─┤ X ├─────»\n",
+       "«                                                              ░ └───┘     »\n",
+       "«                                      ░                                    »\n",
+       "«  q_0: ───────────────────────────────░────────────────────────────────────»\n",
+       "«                                      ░                                    »\n",
+       "«  q_1: ───────────────────────────────░────────────────────────────────────»\n",
+       "«                                      ░                                    »\n",
+       "«  q_2: ──■─────────■──────────────────░────────────────────────────────────»\n",
+       "«         │         │                  ░                                    »\n",
+       "«  q_3: ──┼─────────┼────■─────────■───░────────────────────────────────────»\n",
+       "«         │         │    │         │   ░ ┌───────────┐┌───────────────┐┌───┐»\n",
+       "«  q_4: ──┼─────────┼────┼─────────┼───░─┤ Ry(3pi/8) ├┤ U3(pi/24,0,0) ├┤ X ├»\n",
+       "«         │  ┌───┐  │    │  ┌───┐  │   ░ └───────────┘└───────────────┘└─┬─┘»\n",
+       "«q_a_0: ──┼──┤ X ├──■────┼──┤ X ├──┼───░─────────────────────────────────■──»\n",
+       "«       ┌─┴─┐└───┘  │  ┌─┴─┐└───┘┌─┴─┐ ░                                    »\n",
+       "«q_a_1: ┤ X ├───────┼──┤ X ├─────┤ X ├─░────────────────────────────────────»\n",
+       "«       └─┬─┘     ┌─┴─┐└───┘     └─┬─┘ ░                                    »\n",
+       "«q_a_2: ──■───────┤ X ├────────────■───░────────────────────────────────────»\n",
+       "«                 └───┘                ░                                    »\n",
        "«                                                                            ░ »\n",
        "«  q_0: ─────────────────────────────────────────────────────────────────────░─»\n",
        "«                                                                            ░ »\n",
-       "«  q_1: ────────────────────────────■────────────■───────────────────────■───░─»\n",
-       "«       ┌─────────────────┐       ┌─┴─┐          │                       │   ░ »\n",
-       "«  q_2: ┤ U3(0.28365,0,0) ├───────┤ X ├──────────┼───────────────────────┼───░─»\n",
-       "«       └─────────────────┘┌──────┴───┴───────┐┌─┴─┐┌─────────────────┐┌─┴─┐ ░ »\n",
-       "«  q_3: ───────────────────┤ U3(-0.22349,0,0) ├┤ X ├┤ U3(0.22349,0,0) ├┤ X ├─░─»\n",
-       "«                          └──────────────────┘└───┘└─────────────────┘└───┘ ░ »\n",
-       "«  q_4: ─────────────────────────────────────────────────────────────────────░─»\n",
+       "«  q_1: ─────────────────────────────────────────────────────────────────────░─»\n",
        "«                                                                            ░ »\n",
-       "«q_a_0: ─────────────────────────────────────────────────────────────────────░─»\n",
+       "«  q_2: ─────────────────────────────────────────────────────────────────────░─»\n",
        "«                                                                            ░ »\n",
-       "«q_a_1: ─────────────────────────────────────────────────────────────────────░─»\n",
+       "«  q_3: ─────────────────────────────────────────────────────────────────────░─»\n",
+       "«       ┌────────────────┐┌───┐┌───────────────┐┌───┐┌────────────────┐┌───┐ ░ »\n",
+       "«  q_4: ┤ U3(-pi/24,0,0) ├┤ X ├┤ U3(pi/12,0,0) ├┤ X ├┤ U3(-pi/12,0,0) ├┤ X ├─░─»\n",
+       "«       └────────────────┘└─┬─┘└───────────────┘└─┬─┘└────────────────┘└─┬─┘ ░ »\n",
+       "«q_a_0: ────────────────────■─────────────────────┼──────────────────────┼───░─»\n",
+       "«                                                 │                      │   ░ »\n",
+       "«q_a_1: ──────────────────────────────────────────■──────────────────────■───░─»\n",
        "«                                                                            ░ »\n",
        "«q_a_2: ─────────────────────────────────────────────────────────────────────░─»\n",
        "«                                                                            ░ »\n",
-       "«                                                ░              »\n",
-       "«  q_0: ─────────────────────────────────────────░──────────────»\n",
-       "«                                                ░              »\n",
-       "«  q_1: ─────────────────────────────────────────░──────────────»\n",
-       "«                                                ░              »\n",
-       "«  q_2: ──■────■────■─────────■──────────────────░──────────────»\n",
-       "«         │    │    │         │                  ░              »\n",
-       "«  q_3: ──┼────┼────┼─────────┼────■────■────────░──────────────»\n",
-       "«         │    │    │         │    │    │        ░ ┌───────────┐»\n",
-       "«  q_4: ──┼────┼────┼─────────┼────┼────┼────────░─┤ Ry(3pi/8) ├»\n",
-       "«         │  ┌─┴─┐  │  ┌───┐  │    │    │  ┌───┐ ░ └───────────┘»\n",
-       "«q_a_0: ──■──┤ X ├──┼──┤ X ├──■────┼────┼──┤ X ├─░──────────────»\n",
-       "«         │  └───┘┌─┴─┐└───┘  │  ┌─┴─┐┌─┴─┐└───┘ ░              »\n",
-       "«q_a_1: ──┼───────┤ X ├───────┼──┤ X ├┤ X ├──────░──────────────»\n",
-       "«       ┌─┴─┐     └─┬─┘     ┌─┴─┐└───┘└─┬─┘      ░              »\n",
-       "«q_a_2: ┤ X ├───────■───────┤ X ├───────■────────░──────────────»\n",
-       "«       └───┘               └───┘                ░              »\n",
-       "«                                                                          »\n",
-       "«  q_0: ───────────────────────────────────────────────────────────────────»\n",
-       "«                                                                          »\n",
-       "«  q_1: ───────────────────────────────────────────────────────────────────»\n",
-       "«                                                                          »\n",
-       "«  q_2: ───────────────────────────────────────────────────────────────────»\n",
-       "«                                                                          »\n",
-       "«  q_3: ───────────────────────────────────────────────────────────────────»\n",
-       "«       ┌───────────────┐┌───┐┌────────────────┐┌───┐┌───────────────┐┌───┐»\n",
-       "«  q_4: ┤ U3(pi/24,0,0) ├┤ X ├┤ U3(-pi/24,0,0) ├┤ X ├┤ U3(pi/12,0,0) ├┤ X ├»\n",
-       "«       └───────────────┘└─┬─┘└────────────────┘└─┬─┘└───────────────┘└─┬─┘»\n",
-       "«q_a_0: ───────────────────■──────────────────────■─────────────────────┼──»\n",
-       "«                                                                       │  »\n",
-       "«q_a_1: ────────────────────────────────────────────────────────────────■──»\n",
-       "«                                                                          »\n",
-       "«q_a_2: ───────────────────────────────────────────────────────────────────»\n",
-       "«                                                                          »\n",
-       "«                               ░                                          ░ \n",
-       "«  q_0: ────────────────────────░──────────────────────────────────────────░─\n",
-       "«                               ░                                          ░ \n",
-       "«  q_1: ────────────────────────░──────────────────────────────────────────░─\n",
-       "«                               ░                                          ░ \n",
-       "«  q_2: ────────────────────────░──────────────────■────■─────────■────■───░─\n",
-       "«                               ░                  │    │         │    │   ░ \n",
-       "«  q_3: ────────────────────────░───■─────────■────┼────┼─────────┼────┼───░─\n",
-       "«       ┌────────────────┐┌───┐ ░   │         │    │    │         │    │   ░ \n",
-       "«  q_4: ┤ U3(-pi/12,0,0) ├┤ X ├─░───┼─────────┼────┼────┼─────────┼────┼───░─\n",
-       "«       └────────────────┘└─┬─┘ ░   │  ┌───┐  │    │    │  ┌───┐┌─┴─┐  │   ░ \n",
-       "«q_a_0: ────────────────────┼───░───┼──┤ X ├──┼────■────┼──┤ X ├┤ X ├──■───░─\n",
-       "«                           │   ░ ┌─┴─┐└───┘┌─┴─┐  │  ┌─┴─┐└───┘└───┘  │   ░ \n",
-       "«q_a_1: ────────────────────■───░─┤ X ├─────┤ X ├──┼──┤ X ├────────────┼───░─\n",
-       "«                               ░ └─┬─┘     └───┘┌─┴─┐└─┬─┘          ┌─┴─┐ ░ \n",
-       "«q_a_2: ────────────────────────░───■────────────┤ X ├──■────────────┤ X ├─░─\n",
-       "«                               ░                └───┘               └───┘ ░ </pre>"
+       "«                                                ░ \n",
+       "«  q_0: ─────────────────────────────────────────░─\n",
+       "«                                                ░ \n",
+       "«  q_1: ─────────────────────────────────────────░─\n",
+       "«                                                ░ \n",
+       "«  q_2: ─────────────────■─────────■────■────■───░─\n",
+       "«                        │         │    │    │   ░ \n",
+       "«  q_3: ──■─────────■────┼─────────┼────┼────┼───░─\n",
+       "«         │         │    │         │    │    │   ░ \n",
+       "«  q_4: ──┼─────────┼────┼─────────┼────┼────┼───░─\n",
+       "«         │  ┌───┐  │    │  ┌───┐  │  ┌─┴─┐  │   ░ \n",
+       "«q_a_0: ──┼──┤ X ├──┼────■──┤ X ├──┼──┤ X ├──■───░─\n",
+       "«       ┌─┴─┐└───┘┌─┴─┐  │  └───┘┌─┴─┐└───┘  │   ░ \n",
+       "«q_a_1: ┤ X ├─────┤ X ├──┼───────┤ X ├───────┼───░─\n",
+       "«       └─┬─┘     └───┘┌─┴─┐     └─┬─┘     ┌─┴─┐ ░ \n",
+       "«q_a_2: ──■────────────┤ X ├───────■───────┤ X ├─░─\n",
+       "«                      └───┘               └───┘ ░ </pre>"
       ],
       "text/plain": [
-       "<qiskit.visualization.text.TextDrawing at 0x1d89ace47f0>"
+       "<qiskit.visualization.text.TextDrawing at 0x10fd82210>"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -565,8 +620,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
+   "execution_count": 17,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:52:35.675198Z",
+     "start_time": "2019-12-12T17:52:35.491437Z"
+    }
+   },
    "outputs": [],
    "source": [
     "job = execute(qc, backend=BasicAer.get_backend('statevector_simulator'))"
@@ -574,8 +634,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
+   "execution_count": 18,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:52:35.831081Z",
+     "start_time": "2019-12-12T17:52:35.817604Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -610,8 +675,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 19,
    "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:53:12.143311Z",
+     "start_time": "2019-12-12T17:52:36.882344Z"
+    },
     "scrolled": false
    },
    "outputs": [
@@ -640,8 +709,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
+   "execution_count": 20,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:53:12.539348Z",
+     "start_time": "2019-12-12T17:53:12.232278Z"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -657,7 +731,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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ZpD2BEWVFx0o6oKLaR4CDgEf6LzSzgl15ZXr1qspmTamnD+azpMmUAAEcBiyrqPM3YC5wUv+FZmZmnazXBBMRF5LNaZH0NPDliHg478DMzKyzNTSKLCI89NjMzOpSTx/MKGBGRCzKfu5RRNzWL5GZmVlHq+cOZjKwK/BQ9nNQezRZAH4omZmZ1ZVgtgJeLPvZbNWw005FR2DW0erp5H+22s9mA54fl2zWJ/X0wazdyAUjwpMtzcysriayxaS+lXq5D8bMzOpKMF+nsQRjNjAoG8vS48LhZlZLPX0w17QgDjMzG2D6+shkMzOzqurp5H8IOCoi5kj6Hb00l0XELv0VnJmZda56+mD+BLxd9rMbpM3MrFf19MEcXfbzUblGY2ZmA0bTfTBKNpHU00PIzMxsFdXQasqwYvHL04Gu7PxlkmYB50bEr/s5PrPiTJhQdARmHa2hBCNpHHA5MBU4AfgrMAQ4BPiVpG9GhP+vtIHBj0s265NG72C+B0yMiG9UlF8h6QrgNMAJxszMGu6D2Qi4ucaxm4ANe7uApO0lTZW0RNILks6W1OPyMpKGSYoq26QqdQ+W9IikdyTNkTS6rk9mVmnixLSZWVMavYPpBvYC7qpybC/g3p5OlrQBcDcwBzgY2Aa4iJToTq/j/U8EflO2v6Di+nuQEt3lwPHAKOB6SQsj4s46rm/2vnHj0qubysyaUs9Ey+3Ldn8EXCVpI+AW3u+D+TJwIHBsL5c7DlgLOCQiFgF3SVoPGC/pgqysJ49HxAM9HD8DuDcijs/2uyXtAJwJOMGYmbVQPXcwj/LByZUCxmVb5dMtb6fn1ZQPBO6oSCSTgPNJd0C31hFPVZI+DIwk3bmUmwRcLWlwRLzR7PXNzKwx9SSYkf34ftsB95QXRMRzkpZkx3pLMFdL2pB053Q9cFpElFYZ2AZYA5hbcc5jpCa4TwC/61v4ZmZWL0ULlyKXtBQ4KSIurSifB1wbEd+rcd5mpBFqdwKLgBHAycCdEXFwVmd3YAbwmYh4uOzcjwN/Bvav1g8jaSwwFmDo0KFdkyatNG4gV4sXL2bQoEEtfc96tXNskH98I0am71bTursBeGT++zfAO24+uNDY+qqd42vn2KC94ysitpEjR86KiOFVD0ZEUxvprmDtyq2Xc5YCJ1Qpn0+aqNnI+3+D1ET3D9n+7tn+f6uot21Wvl9v1+zq6opW6+7ubvl71qudY4toQXzpSTArdrc8efKKrTer/O+uD9o5toj2jq+I2ICZUeNvakPDlLPlYU6W9GSWLN6ssvVkIbB+lfLBwOuNxALcmL3uVHZtqly/tN/o9c3MrA8anQdzPHAK8BNS5/65wNnAE8AzZE1NPZhL6mtZQdIWwDqs3HfSm6h4fYqU9LarqLcdsDyL0ax+pXsYM2tKowlmDPB94IJs/5aIOAvYgZQgtu3l/CnA/pLWLSsbTXocwPQGYzk0e50FEBHvkubpHFZRbzRwf3gEmZlZSzU60XIr4OGIeC/rsF8fICKWS7ocuIp0h1PLFaS7oJslnQ9sDYwHLo6yoctZE9z0iDgm2x8PrEuaZLkI2BM4Cbg5Iv5Ydv1zgGmSLiXN0xmVbQc0+DnNzKyPGr2DeRUoDVF4DvhM2bENSJMoa4qIhcA+pLkytwJnAZeQ7orKrc4H59PMJc2TuRq4DTgCuDB7Lb/+DNKdzb7AHcAXgSPCs/itGV1daTOzpjR6B/MbYGfSH/nrSDPwNwT+BnyLtMpyjyJiDrB3L3WGVexPIk2Y7FVE3EK6ezHrm9mzi47ArKM1mmDGA5tnP59HaiI7inTnchfw7f4KzMzMOltDCSYiHgcez35+l/RMmBNyiMvMzDpcw0+0LJH0MWAz4IWImN9/IZmZ2UDQaCc/kr4h6XngWeBB4DlJ8yR9s9+jMzOzjtXoTP4zgctI81kOAoZnr1OAH2XHzczMGm4i+xZwXkScUVF+u6SXs+Nn90tkZkUbM6boCMw6WqMJZi1qP7VyOh5FZgOJH5ds1ieN9sHcAhxS49hXgMl9C8fMzAaKeh6ZPKpsdwpwgaRhrPzI5B2A7/Z/iGYFmTUrvXo2v1lT6mkim8zKj0beHNi/St3/R3rSpFnnG549Q8krKps1pZ4Es1XuUZiZ2YDTa4KJiGdbEYiZmQ0sDc/kl7Q6qUN/D2BD4DXgPtLS+cv6NzwzM+tUDSUYSUOAO4FPk55g+TKwG2n+yx8kfSEiXunvIM3MrPM0Okz5YmAj4LMRsXVE7BYRWwOfzcov7u8AzcysMzWaYEYBJ0fE78oLs/1TScvGmJmZNdwH82HgzRrH3gTW7Fs4Zm1k5syiIzDraI0mmAeAkyXdExFvlQolrQOcnB03Gxg8wdKsTxpNMN8BuoHnJd1J6uQfQpp0KWBEv0ZnZmYdq6E+mIh4GNgWmAhsAuxHSjBXANtGxB/6PUKzoowdmzYza0rddzCS1gB2AZ6OiFPyC8msTVx5ZXr1qspmTWnkDuY94B7g73OKxczMBpC6E0xELAf+DAzNLxwzMxsoGp0HcxpwpqQd8wjGzMwGjkZHkZ1OmrH/sKT5pFFkH1jLPCJ26afYzMysgzWaYB7NNjMzsx7VlWAkrUVaJuZR4CXg7oh4Oc/AzAq3005FR2DW0ep5ZPLWwN3AsLLiRZK+GhF35hWYWeFKj0w2s6bU08l/AbAc+DywNrAD8HtgQo5xmZlZh6snwewGnB4Rv4mIdyLiMWAc8HeSNss3PDMz61T1JJjNgL9UlD1FWnts036PyKxdSGkzs6bUOw8meq9iZmb2vnqHKd8haVmV8qmV5RExpO9hmZlZp6snwZyVexRmZjbg9JpgIsIJxszMGtboWmRmZmZ1cYIxM7NcNLoWmdmqY4LnEpv1hROMWS1+XLJZn7iJzMzMcuEEY1bLxIlpM7OmtDzBSNpe0lRJSyS9IOlsSav1cs7Okq6W9GR23uOSvi/pIxX1xkuKKtsB+X4qG5DGjUubmTWlpX0wkjYgLf0/BzgY2Aa4iJToTu/h1NFZ3fOBPwOfBs7JXr9SUfcNoDKhPNbX2M3MrDGt7uQ/DlgLOCQiFgF3SVoPGC/pgqysmvMj4pWy/WmS3gEmSNoyIp4tO7YsIh7IJ3wzM6tXq5vIDgTuqEgkk0hJZ69aJ1Ukl5LfZ69e+8zMrA21OsFsB8wtL4iI54Al2bFGfI70ILTHK8rXl7RA0lJJv5d0SNPRmplZ0xTRupX4JS0FToqISyvK5wHXRsT36rzOpsAfgdsi4qiy8iNJdzQPA4NID0YbBXwlIm6uca2xwFiAoUOHdk2aNKnRj9UnixcvZtCgQS19z3q1c2yQf3wjRo4EYFp3NwCPzH9jxbEdNx9caGx91c7xtXNs0N7xFRHbyJEjZ0XE8KoHI6JlG7AUOKFK+Xzg3DqvsSZwL+khaBv0UlfA/cDD9Vy7q6srWq27u7vl71mvdo4togXxQdoyW548ecXWm1X+d9cH7RxbRHvHV0RswMyo8Te11U1kC4H1q5QPBl7v7WRJAq4FdgBGRcTCnupnH/5m4NO9DYU2W0kpxZhZU1o9imwuFX0tkrYA1qGib6aGS0jDm/eLiHrql/ivhJlZi7X6DmYKsL+kdcvKRgNvA9N7OlHSqcC3gSMjYkY9b5bd8XwZ+ENEvNdcyGZm1oxW38FcARwP3CzpfGBrYDxwcZQNXZb0JDA9Io7J9o8AzgOuAeZL2rXsmk9FNoxZ0nTgJtLd0DrAGGBX4Ev5fiwbkLq60uusWcXGYdahWppgImKhpH2Ay4BbSf0ul5CSTGVc5X0mX8hej8q2ckeTEg/Ak8C/AJuRhjDPBg6KiCn9Eb+tYmbPLjoCs47W8uX6I2IOsHcvdYZV7B/Fyoml2nnH9CE0MzPrR15N2czMcuEEY2ZmuXCCMTOzXDjBmJlZLlreyW/WMcaMKToCs47mBGNWix+XbNYnbiIzM7NcOMGY1TJrlmfxm/WBm8jMahmePeLCKyqbNcV3MGZmlgsnGDMzy4UTjJmZ5cIJxszMcuEEY2ZmuXCCMTOzXHiYslktM2cWHYFZR3OCMaul9MhkM2uKm8jMzCwXTjBmtYwdmzYza4oTjFktV16ZNjNrihOMmZnlwgnGzMxy4QRjZma5cIIxM7NcOMGYmVkuPNHSrJaddio6ArOO5gRjVosfl2zWJ24iMzOzXDjBmJlZLpxgzGqR0mZmTXGCMTOzXDjBmJlZLpxgzMwsF04wZmaWCycYMzPLhROMmZnlwjP5zWqZMKHoCMw6mhOMWS1+XLJZn7iJzMzMcuEEY1bLxIlpM7OmuInMrJZx49Krm8rMmuI7GDMzy0XLE4yk7SVNlbRE0guSzpa0Wh3nDZZ0taSFkt6Q9HNJG1Wpd7CkRyS9I2mOpNH5fBIzM+tJSxOMpA2Au4EADgbOBr4DnFXH6TcAI4BjgaOAnYFbKq6/B3AT0A0cCPwauF7SF/rlA1hbeWT+Gww75ddFh2FmNbS6D+Y4YC3gkIhYBNwlaT1gvKQLsrKVSNoN2B/YKyLuzcrmAw9K2jci7s6qngHcGxHHZ/vdknYAzgTuzO9jmeWnlESf+cFBBUdi1phWN5EdCNxRkUgmkZLOXr2c93IpuQBExEPA09kxJH0YGAn8ouLcScBukgb3PXwzM6tXq+9gtgPuKS+IiOckLcmO3drDeXOrlD+WHQPYBlijSr3HSIn0E8Dvmgt74MnzW7G/cXe+8qbHVv13LL3nNQes05L3s/y1OsFsALxepXxhdqyZ87Yuq0OVegsrjn+ApLFAaRzqYkmP9xBHHjYGFrT4PVfQ+T0e7lNsvVy7P2wMLMj9fao81bKO9+z3/679/Dnrjq8F/x0/YOT5xf4/UYd2jq+I2LasdaCIeTBRpUw1yps5r3JfNcpTYcREoLDZdJJmRsTwot6/J+0cG7R3fO0cG7R3fO0cG7R3fO0WW6v7YBYC61cpH0z1O5Tezlu/7LyFZWWVdejl+mZm1s9anWDm8n6fCQCStgDWoXofS83zMuV9M08BS6vU2w5YDjzRRLxmZtakVieYKcD+ktYtKxsNvA1M7+W8TbN5LgBIGk7qf5kCEBHvkua/HFZx7mjg/oh4o+/h56KdF7tq59igveNr59igveNr59igveNrq9gU0VvXRz++WZpoOQd4FDiflCAuBi6NiNPL6j0JTI+IY8rKbieNBDuRdEdyPvDXiPh8WZ09gGnAZaRJmKOy+gdEhOfBmJm1UEvvYCJiIbAPsBppSPJZwCXA9yuqrp7VKXc46S7np8C1wCzgyxXXnwEcCuwL3AF8ETjCycXMrPVaegdjZmarDq+m3AYkrSfpLEkPZQt5viTpl5I+UXRsJZJGS7pZ0ouSQtJRBcXR1GKprSDp45ImSPqDpPckTSs6phJJh0n6laT5khZLmiXpn4qOq0TSoZJ+K+nVbKHaxyWdLmnNomOrJGnz7HcYkga1QTxHZbFUbscVHZufB9Me/g4YA/wEOA1YGziVtNbapyPi+SKDyxwKDAMmkxYcbbmyxVLnkBZL3Qa4iPRF6fQeTm2VHUj9fg8A7faH8X+Tllb6X6SJeKOA6yRtHBE/LjSyZCPSIJ0LSVMKdgHGA5sC/7O4sKq6EFhMGv3aTvYmDZgq+UtRgZS4iawNSFoHWB4Rb5eVbQg8B1wYEfWsNp0rSR+KiOXZN7Y3gaMj4poWx3Aq8F1gy9J6dpK+S/aHqNZiqa1S+h1lP98IbBwRI4qMqSRLJAsqyq4DdouIrQoKq0eSzgW+BWwQbfKHStLngf8CziMlmnUjYnHBMR0FXN0OsVRyE1kbiIi3ypNLVvYa8CwwpJioPqj0h7NgzS6W2hJt8juqqjK5ZH5Pm/z7quFV2uhOMGuK/THpMSPtulRMW3GCaVOSNgE+TmoOsmSlRU8j4jmgtFiqNeZztNm/L0mrSVo7m3JwPPDv7XL3QnrcyEeA/1t0IDU8JWlZ1n81ruhgwH0w7ewiUjvvpKIDaSPNLpZqFSTtQ+rH+nrRsVR4C/hw9vO1wEkFxrJC9vTcc4AjI2KpqiyAWqAXSc/Ceog0veOfgCskrR0RlxQZmBNMTrLnz2zWW72IWGmJHEnfAI4EvhIRr+YQXp/iK1izi6VaRtIw4Drgv1rdjzoObm4AAAJdSURBVFaHz5EGuexCelDgZcA3C40oORd4MCJuKzqQShFxB2neX8mU7PlYp0v6tyKbbp1g8nMYcGUd9T7wVUjSF0ntvCdHxC/zCCzTVHwFa3axVMtkg0emkAaQHFlwOCuJiNnZjzMkLQB+JumiiHiqqJiyp+J+HdhTUunf39rZ62BJ71X2obaBG4GvkkZ+FjaazH0wOYmIqyJCvW3l50j6HKlJ7IqIuLDd4msDzS6WaoCktUnDzNcEDoqItwoOqTelZFP0KLdtSQ8zvJ/0JWch7/fDzCN9IWxXhd7Z+w6mTWTfkiYDt5M6N21lU4CTJK0bEW9mZfUslrrKk7Q68J+kP5a7R8RfCw6pHrtnr08XGgXMID2OvdwBwMmk+USFzzep4iukkW7PFhmEE0wbkDSElFgWAz8CdinrRFwUEYWP9JG0PbA9aRQNwHBJi4FXIqJVf9yvICXfmyWVFksdD1xc9BwYWHGHMCrb3RxYT9Kh2f5tEbGkmMgAuJwU2wnAhpJ2LTv2+2w18sJki9neDfwJeI+UXL4D3FBk8xisGOI9rbws68cCuK/ouSeSbiJ18P+R1Mk/OtuOL3rovCdatgFJI0izmKuZ3g6T9SSNZ+VFSaHF8WWJ7jJgN1K/y1XA+Ih4r1Ux1JL90an1bXuriHimZcFUkPQMtR9tW2hsAJLOIS1eOwxYRroruJrUXLy0wNCqaqfJjZLOI92xbEHqM51DWqH+P4qMC5xgzMwsJ+7kNzOzXDjBmJlZLpxgzMwsF04wZmaWCycYMzPLhROMmZnlwgnGzMxy4QRjZma5+P8UW75BItdh7wAAAABJRU5ErkJggg==\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -720,8 +794,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
+   "execution_count": 21,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:53:12.630621Z",
+     "start_time": "2019-12-12T17:53:12.625898Z"
+    }
+   },
    "outputs": [],
    "source": [
     "# define value x to evaluate the CDF(x)\n",
@@ -745,8 +824,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
+   "execution_count": 22,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:53:12.722599Z",
+     "start_time": "2019-12-12T17:53:12.719444Z"
+    }
+   },
    "outputs": [],
    "source": [
     "# set x value to estimate the CDF\n",
@@ -755,8 +839,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
+   "execution_count": 23,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:53:12.828110Z",
+     "start_time": "2019-12-12T17:53:12.806429Z"
+    }
+   },
    "outputs": [],
    "source": [
     "# get operator\n",
@@ -776,17 +865,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
+   "execution_count": 25,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:53:13.196976Z",
+     "start_time": "2019-12-12T17:53:13.140090Z"
+    }
+   },
    "outputs": [],
    "source": [
-    "job = execute(qc, backend=BasicAer.get_backend('statevector_simulator'))"
+    "job = execute(qc, backend=Aer.get_backend('statevector_simulator'))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 26,
    "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:53:13.308938Z",
+     "start_time": "2019-12-12T17:53:13.276423Z"
+    },
     "scrolled": true
    },
    "outputs": [
@@ -827,80 +925,80 @@
        "«                                                                           »\n",
        "«q_a_2: ────────────────────────────────────────────────────────────────────»\n",
        "«                                                                           »\n",
-       "«                                                             »\n",
-       "«  q_0: ──■────■───────────────────────────────────────────■──»\n",
-       "«         │    │                                           │  »\n",
-       "«  q_1: ──┼────┼───────────────────────────────■───────────┼──»\n",
-       "«       ┌─┴─┐  │  ┌──────────────────┐       ┌─┴─┐         │  »\n",
-       "«  q_2: ┤ X ├──┼──┤ U3(-0.28365,0,0) ├───────┤ X ├─────────┼──»\n",
-       "«       └───┘┌─┴─┐└──────────────────┘┌──────┴───┴──────┐┌─┴─┐»\n",
-       "«  q_3: ─────┤ X ├────────────────────┤ U3(0.11174,0,0) ├┤ X ├»\n",
-       "«            └───┘                    └─────────────────┘└───┘»\n",
-       "«  q_4: ──────────────────────────────────────────────────────»\n",
-       "«                                                             »\n",
-       "«q_a_0: ──────────────────────────────────────────────────────»\n",
-       "«                                                             »\n",
-       "«q_a_1: ──────────────────────────────────────────────────────»\n",
-       "«                                                             »\n",
-       "«q_a_2: ──────────────────────────────────────────────────────»\n",
-       "«                                                             »\n",
-       "«                                                                            ░ »\n",
-       "«  q_0: ─────────────────────────────────────────────────────────────────────░─»\n",
-       "«                                                                            ░ »\n",
-       "«  q_1: ────────────────────────────■────────────■───────────────────────■───░─»\n",
-       "«       ┌─────────────────┐       ┌─┴─┐          │                       │   ░ »\n",
-       "«  q_2: ┤ U3(0.28365,0,0) ├───────┤ X ├──────────┼───────────────────────┼───░─»\n",
-       "«       └─────────────────┘┌──────┴───┴───────┐┌─┴─┐┌─────────────────┐┌─┴─┐ ░ »\n",
-       "«  q_3: ───────────────────┤ U3(-0.22349,0,0) ├┤ X ├┤ U3(0.22349,0,0) ├┤ X ├─░─»\n",
-       "«                          └──────────────────┘└───┘└─────────────────┘└───┘ ░ »\n",
-       "«  q_4: ─────────────────────────────────────────────────────────────────────░─»\n",
-       "«                                                                            ░ »\n",
-       "«q_a_0: ─────────────────────────────────────────────────────────────────────░─»\n",
-       "«                                                                            ░ »\n",
-       "«q_a_1: ─────────────────────────────────────────────────────────────────────░─»\n",
-       "«                                                                            ░ »\n",
-       "«q_a_2: ─────────────────────────────────────────────────────────────────────░─»\n",
-       "«                                                                            ░ »\n",
-       "«                                                ░                 ░           »\n",
-       "«  q_0: ─────────────────────────────────────────░─────────────────░───────────»\n",
-       "«                                                ░                 ░           »\n",
-       "«  q_1: ─────────────────────────────────────────░─────────────────░───────────»\n",
-       "«                                                ░                 ░           »\n",
-       "«  q_2: ──■────■────■─────────■──────────────────░─────────────────░───────────»\n",
-       "«         │    │    │         │                  ░                 ░           »\n",
-       "«  q_3: ──┼────┼────┼─────────┼────■────■────────░─────────────────░───■───────»\n",
-       "«         │    │    │         │    │    │        ░      ┌───┐┌───┐ ░   │       »\n",
-       "«  q_4: ──┼────┼────┼─────────┼────┼────┼────────░──────┤ X ├┤ X ├─░───┼───────»\n",
-       "«         │  ┌─┴─┐  │  ┌───┐  │    │    │  ┌───┐ ░      └─┬─┘└───┘ ░   │  ┌───┐»\n",
-       "«q_a_0: ──■──┤ X ├──┼──┤ X ├──■────┼────┼──┤ X ├─░───■────┼────■───░───┼──┤ X ├»\n",
-       "«         │  └───┘┌─┴─┐└───┘  │  ┌─┴─┐┌─┴─┐└───┘ ░   │    │    │   ░ ┌─┴─┐└───┘»\n",
-       "«q_a_1: ──┼───────┤ X ├───────┼──┤ X ├┤ X ├──────░───┼────■────┼───░─┤ X ├─────»\n",
-       "«       ┌─┴─┐     └─┬─┘     ┌─┴─┐└───┘└─┬─┘      ░ ┌─┴─┐  │  ┌─┴─┐ ░ └─┬─┘     »\n",
-       "«q_a_2: ┤ X ├───────■───────┤ X ├───────■────────░─┤ X ├──■──┤ X ├─░───■───────»\n",
-       "«       └───┘               └───┘                ░ └───┘     └───┘ ░           »\n",
-       "«                                      ░ \n",
-       "«  q_0: ───────────────────────────────░─\n",
-       "«                                      ░ \n",
-       "«  q_1: ───────────────────────────────░─\n",
-       "«                                      ░ \n",
-       "«  q_2: ───────■────■─────────■────■───░─\n",
-       "«              │    │         │    │   ░ \n",
-       "«  q_3: ──■────┼────┼─────────┼────┼───░─\n",
-       "«         │    │    │         │    │   ░ \n",
-       "«  q_4: ──┼────┼────┼─────────┼────┼───░─\n",
-       "«         │    │    │  ┌───┐┌─┴─┐  │   ░ \n",
-       "«q_a_0: ──┼────■────┼──┤ X ├┤ X ├──■───░─\n",
-       "«       ┌─┴─┐  │  ┌─┴─┐└───┘└───┘  │   ░ \n",
-       "«q_a_1: ┤ X ├──┼──┤ X ├────────────┼───░─\n",
-       "«       └───┘┌─┴─┐└─┬─┘          ┌─┴─┐ ░ \n",
-       "«q_a_2: ─────┤ X ├──■────────────┤ X ├─░─\n",
-       "«            └───┘               └───┘ ░ </pre>"
+       "«                                                                           »\n",
+       "«  q_0: ──■────────────────────────■────────────────────────────────────────»\n",
+       "«         │                        │                                        »\n",
+       "«  q_1: ──┼────────────────────────┼───────────■────────────────────────────»\n",
+       "«       ┌─┴─┐┌──────────────────┐  │         ┌─┴─┐       ┌─────────────────┐»\n",
+       "«  q_2: ┤ X ├┤ U3(-0.28365,0,0) ├──┼─────────┤ X ├───────┤ U3(0.28365,0,0) ├»\n",
+       "«       └───┘└──────────────────┘┌─┴─┐┌──────┴───┴──────┐└─────────────────┘»\n",
+       "«  q_3: ─────────────────────────┤ X ├┤ U3(0.11174,0,0) ├───────────────────»\n",
+       "«                                └───┘└─────────────────┘                   »\n",
+       "«  q_4: ────────────────────────────────────────────────────────────────────»\n",
+       "«                                                                           »\n",
+       "«q_a_0: ────────────────────────────────────────────────────────────────────»\n",
+       "«                                                                           »\n",
+       "«q_a_1: ────────────────────────────────────────────────────────────────────»\n",
+       "«                                                                           »\n",
+       "«q_a_2: ────────────────────────────────────────────────────────────────────»\n",
+       "«                                                                           »\n",
+       "«                                                              ░           »\n",
+       "«  q_0: ──■────────────────────────────────────────────────────░───────────»\n",
+       "«         │                                                    ░           »\n",
+       "«  q_1: ──┼───────────■────────────■───────────────────────■───░───────────»\n",
+       "«         │         ┌─┴─┐          │                       │   ░           »\n",
+       "«  q_2: ──┼─────────┤ X ├──────────┼───────────────────────┼───░───■────■──»\n",
+       "«       ┌─┴─┐┌──────┴───┴───────┐┌─┴─┐┌─────────────────┐┌─┴─┐ ░   │    │  »\n",
+       "«  q_3: ┤ X ├┤ U3(-0.22349,0,0) ├┤ X ├┤ U3(0.22349,0,0) ├┤ X ├─░───┼────┼──»\n",
+       "«       └───┘└──────────────────┘└───┘└─────────────────┘└───┘ ░   │    │  »\n",
+       "«  q_4: ───────────────────────────────────────────────────────░───┼────┼──»\n",
+       "«                                                              ░   │  ┌─┴─┐»\n",
+       "«q_a_0: ───────────────────────────────────────────────────────░───■──┤ X ├»\n",
+       "«                                                              ░   │  └───┘»\n",
+       "«q_a_1: ───────────────────────────────────────────────────────░───┼───────»\n",
+       "«                                                              ░ ┌─┴─┐     »\n",
+       "«q_a_2: ───────────────────────────────────────────────────────░─┤ X ├─────»\n",
+       "«                                                              ░ └───┘     »\n",
+       "«                                      ░                 ░                     »\n",
+       "«  q_0: ───────────────────────────────░─────────────────░─────────────────────»\n",
+       "«                                      ░                 ░                     »\n",
+       "«  q_1: ───────────────────────────────░─────────────────░─────────────────────»\n",
+       "«                                      ░                 ░                     »\n",
+       "«  q_2: ──■─────────■──────────────────░─────────────────░──────────────────■──»\n",
+       "«         │         │                  ░                 ░                  │  »\n",
+       "«  q_3: ──┼─────────┼────■─────────■───░─────────────────░───■─────────■────┼──»\n",
+       "«         │         │    │         │   ░      ┌───┐┌───┐ ░   │         │    │  »\n",
+       "«  q_4: ──┼─────────┼────┼─────────┼───░──────┤ X ├┤ X ├─░───┼─────────┼────┼──»\n",
+       "«         │  ┌───┐  │    │  ┌───┐  │   ░      └─┬─┘└───┘ ░   │  ┌───┐  │    │  »\n",
+       "«q_a_0: ──┼──┤ X ├──■────┼──┤ X ├──┼───░───■────┼────■───░───┼──┤ X ├──┼────■──»\n",
+       "«       ┌─┴─┐└───┘  │  ┌─┴─┐└───┘┌─┴─┐ ░   │    │    │   ░ ┌─┴─┐└───┘┌─┴─┐  │  »\n",
+       "«q_a_1: ┤ X ├───────┼──┤ X ├─────┤ X ├─░───┼────■────┼───░─┤ X ├─────┤ X ├──┼──»\n",
+       "«       └─┬─┘     ┌─┴─┐└───┘     └─┬─┘ ░ ┌─┴─┐  │  ┌─┴─┐ ░ └─┬─┘     └───┘┌─┴─┐»\n",
+       "«q_a_2: ──■───────┤ X ├────────────■───░─┤ X ├──■──┤ X ├─░───■────────────┤ X ├»\n",
+       "«                 └───┘                ░ └───┘     └───┘ ░                └───┘»\n",
+       "«                            ░ \n",
+       "«  q_0: ─────────────────────░─\n",
+       "«                            ░ \n",
+       "«  q_1: ─────────────────────░─\n",
+       "«                            ░ \n",
+       "«  q_2: ───────■────■────■───░─\n",
+       "«              │    │    │   ░ \n",
+       "«  q_3: ───────┼────┼────┼───░─\n",
+       "«              │    │    │   ░ \n",
+       "«  q_4: ───────┼────┼────┼───░─\n",
+       "«       ┌───┐  │  ┌─┴─┐  │   ░ \n",
+       "«q_a_0: ┤ X ├──┼──┤ X ├──■───░─\n",
+       "«       └───┘┌─┴─┐└───┘  │   ░ \n",
+       "«q_a_1: ─────┤ X ├───────┼───░─\n",
+       "«            └─┬─┘     ┌─┴─┐ ░ \n",
+       "«q_a_2: ───────■───────┤ X ├─░─\n",
+       "«                      └───┘ ░ </pre>"
       ],
       "text/plain": [
-       "<qiskit.visualization.text.TextDrawing at 0x1d8a5b32940>"
+       "<qiskit.visualization.text.TextDrawing at 0x1a2f188c10>"
       ]
      },
-     "execution_count": 21,
+     "execution_count": 26,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -911,8 +1009,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
+   "execution_count": 27,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:53:13.404462Z",
+     "start_time": "2019-12-12T17:53:13.395230Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -944,8 +1047,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
+   "execution_count": 28,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:53:25.281790Z",
+     "start_time": "2019-12-12T17:53:13.489005Z"
+    }
+   },
    "outputs": [],
    "source": [
     "# run amplitude estimation\n",
@@ -957,8 +1065,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
+   "execution_count": 29,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:53:25.534493Z",
+     "start_time": "2019-12-12T17:53:25.377374Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -1011,8 +1124,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
+   "execution_count": 30,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:53:25.664138Z",
+     "start_time": "2019-12-12T17:53:25.655593Z"
+    }
+   },
    "outputs": [],
    "source": [
     "def run_ae_for_cdf(x_eval, num_eval_qubits=3, simulator='statevector_simulator'):\n",
@@ -1027,8 +1145,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
+   "execution_count": 31,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:53:25.777242Z",
+     "start_time": "2019-12-12T17:53:25.764174Z"
+    }
+   },
    "outputs": [],
    "source": [
     "def bisection_search(objective, target_value, low_level, high_level, low_value=None, high_value=None):\n",
@@ -1095,8 +1218,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
+   "execution_count": 32,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:53:54.092265Z",
+     "start_time": "2019-12-12T17:53:25.882181Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -1125,8 +1253,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
+   "execution_count": 33,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:53:54.206924Z",
+     "start_time": "2019-12-12T17:53:54.201136Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -1165,8 +1298,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
+   "execution_count": 34,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:53:54.324451Z",
+     "start_time": "2019-12-12T17:53:54.318093Z"
+    }
+   },
    "outputs": [],
    "source": [
     "# define linear objective\n",
@@ -1192,8 +1330,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
+   "execution_count": 35,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:53:54.430712Z",
+     "start_time": "2019-12-12T17:53:54.425804Z"
+    }
+   },
    "outputs": [],
    "source": [
     "var = 2"
@@ -1201,8 +1344,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
+   "execution_count": 36,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:53:54.533399Z",
+     "start_time": "2019-12-12T17:53:54.529484Z"
+    }
+   },
    "outputs": [],
    "source": [
     "multivariate_cvar = MultivariateProblem(u, agg, cvar_objective)"
@@ -1210,8 +1358,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
+   "execution_count": 37,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:53:54.647686Z",
+     "start_time": "2019-12-12T17:53:54.619286Z"
+    }
+   },
    "outputs": [],
    "source": [
     "num_qubits = multivariate_cvar.num_target_qubits\n",
@@ -1233,8 +1386,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
+   "execution_count": 38,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:53:55.069620Z",
+     "start_time": "2019-12-12T17:53:54.764564Z"
+    }
+   },
    "outputs": [],
    "source": [
     "job = execute(qc, backend=BasicAer.get_backend('statevector_simulator'))"
@@ -1242,8 +1400,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
+   "execution_count": 39,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:53:55.239666Z",
+     "start_time": "2019-12-12T17:53:55.217521Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -1279,8 +1442,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
+   "execution_count": 40,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:59:28.975921Z",
+     "start_time": "2019-12-12T17:53:55.370591Z"
+    }
+   },
    "outputs": [],
    "source": [
     "# run amplitude estimation\n",
@@ -1292,8 +1460,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {},
+   "execution_count": 41,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:59:29.082789Z",
+     "start_time": "2019-12-12T17:59:29.077755Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -1314,8 +1487,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {},
+   "execution_count": 42,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:59:29.601663Z",
+     "start_time": "2019-12-12T17:59:29.208078Z"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -1368,19 +1546,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 43,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2019-08-22T01:56:12.651056Z",
-     "start_time": "2019-08-22T01:56:12.640412Z"
+     "end_time": "2019-12-12T17:59:29.776957Z",
+     "start_time": "2019-12-12T17:59:29.705383Z"
     }
    },
    "outputs": [
     {
      "data": {
       "text/html": [
-       "<h3>Version Information</h3><table><tr><th>Qiskit Software</th><th>Version</th></tr><tr><td>Qiskit</td><td>None</td></tr><tr><td>Terra</td><td>0.9.0</td></tr><tr><td>Aer</td><td>0.3.0</td></tr><tr><td>Ignis</td><td>0.2.0</td></tr><tr><td>Aqua</td><td>0.5.6</td></tr><tr><td>IBM Q Provider</td><td>0.3.2rc1</td></tr><tr><th>System information</th></tr><tr><td>Python</td><td>3.7.4 (default, Aug 13 2019, 15:17:50) \n",
-       "[Clang 4.0.1 (tags/RELEASE_401/final)]</td></tr><tr><td>OS</td><td>Darwin</td></tr><tr><td>CPUs</td><td>4</td></tr><tr><td>Memory (Gb)</td><td>16.0</td></tr><tr><td colspan='2'>Wed Aug 21 21:56:12 2019 EDT</td></tr></table>"
+       "<h3>Version Information</h3><table><tr><th>Qiskit Software</th><th>Version</th></tr><tr><td>Qiskit</td><td>0.14.0</td></tr><tr><td>Terra</td><td>0.11.0</td></tr><tr><td>Aer</td><td>0.3.4</td></tr><tr><td>Ignis</td><td>0.2.0</td></tr><tr><td>Aqua</td><td>0.6.1</td></tr><tr><td>IBM Q Provider</td><td>0.4.4</td></tr><tr><th>System information</th></tr><tr><td>Python</td><td>3.7.5 (default, Oct 25 2019, 10:52:18) \n",
+       "[Clang 4.0.1 (tags/RELEASE_401/final)]</td></tr><tr><td>OS</td><td>Darwin</td></tr><tr><td>CPUs</td><td>4</td></tr><tr><td>Memory (Gb)</td><td>16.0</td></tr><tr><td colspan='2'>Thu Dec 12 12:59:29 2019 EST</td></tr></table>"
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -1432,7 +1610,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.4"
+   "version": "3.7.5"
   },
   "varInspector": {
    "cols": {
diff --git a/qiskit/advanced/aqua/finance/simulation/.ipynb_checkpoints/european_put_option_pricing-checkpoint.ipynb b/qiskit/advanced/aqua/finance/simulation/.ipynb_checkpoints/european_put_option_pricing-checkpoint.ipynb
index 920cb7da4..07776128f 100644
--- a/qiskit/advanced/aqua/finance/simulation/.ipynb_checkpoints/european_put_option_pricing-checkpoint.ipynb
+++ b/qiskit/advanced/aqua/finance/simulation/.ipynb_checkpoints/european_put_option_pricing-checkpoint.ipynb
@@ -1,25 +1,22 @@
 {
  "cells": [
   {
+   "attachments": {
+    "qiskit_header.png": {
+     "image/png": 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"
+    }
+   },
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<img src=\"../../../../../images/qiskit_header.png\" alt=\"Note: In order for images to show up in this jupyter notebook you need to select File => Trusted Notebook\" align=\"middle\">"
+    "![qiskit_header.png](attachment:qiskit_header.png)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# _*Qiskit Finance: Pricing European Put Options*_ \n",
-    "\n",
-    "The latest version of this notebook is available on https://github.com/Qiskit/qiskit-iqx-tutorials.\n",
-    "\n",
-    "***\n",
-    "### Contributors\n",
-    "Stefan Woerner<sup>[1]</sup>, Daniel Egger<sup>[1]</sup>, Shaohan Hu<sup>[1]</sup>, Stephen Wood<sup>[1]</sup>, Marco Pistoia<sup>[1]</sup>\n",
-    "### Affliation\n",
-    "- <sup>[1]</sup>IBMQ"
+    "# Qiskit Finance: Pricing European Put Options"
    ]
   },
   {
@@ -487,40 +484,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2019-08-22T01:56:34.210824Z",
-     "start_time": "2019-08-22T01:56:34.200032Z"
+     "start_time": "2019-12-12T17:54:26.669Z"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<h3>Version Information</h3><table><tr><th>Qiskit Software</th><th>Version</th></tr><tr><td>Qiskit</td><td>None</td></tr><tr><td>Terra</td><td>0.9.0</td></tr><tr><td>Aer</td><td>0.3.0</td></tr><tr><td>Ignis</td><td>0.2.0</td></tr><tr><td>Aqua</td><td>0.5.6</td></tr><tr><td>IBM Q Provider</td><td>0.3.2rc1</td></tr><tr><th>System information</th></tr><tr><td>Python</td><td>3.7.4 (default, Aug 13 2019, 15:17:50) \n",
-       "[Clang 4.0.1 (tags/RELEASE_401/final)]</td></tr><tr><td>OS</td><td>Darwin</td></tr><tr><td>CPUs</td><td>4</td></tr><tr><td>Memory (Gb)</td><td>16.0</td></tr><tr><td colspan='2'>Wed Aug 21 21:56:34 2019 EDT</td></tr></table>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div style='width: 100%; background-color:#d5d9e0;padding-left: 10px; padding-bottom: 10px; padding-right: 10px; padding-top: 5px'><h3>This code is a part of Qiskit</h3><p>&copy; Copyright IBM 2017, 2019.</p><p>This code is licensed under the Apache License, Version 2.0. You may<br>obtain a copy of this license in the LICENSE.txt file in the root directory<br> of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.<p>Any modifications or derivative works of this code must retain this<br>copyright notice, and modified files need to carry a notice indicating<br>that they have been altered from the originals.</p></div>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "import qiskit.tools.jupyter\n",
     "%qiskit_version_table\n",
@@ -551,7 +521,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.4"
+   "version": "3.7.5"
   },
   "varInspector": {
    "cols": {
diff --git a/qiskit/advanced/aqua/finance/simulation/.ipynb_checkpoints/fixed_income_pricing-checkpoint.ipynb b/qiskit/advanced/aqua/finance/simulation/.ipynb_checkpoints/fixed_income_pricing-checkpoint.ipynb
index 1c7ca6f25..81aa37c03 100644
--- a/qiskit/advanced/aqua/finance/simulation/.ipynb_checkpoints/fixed_income_pricing-checkpoint.ipynb
+++ b/qiskit/advanced/aqua/finance/simulation/.ipynb_checkpoints/fixed_income_pricing-checkpoint.ipynb
@@ -1,25 +1,22 @@
 {
  "cells": [
   {
+   "attachments": {
+    "qiskit_header.png": {
+     "image/png": 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"
+    }
+   },
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<img src=\"../../../../../images/qiskit_header.png\" alt=\"Note: In order for images to show up in this jupyter notebook you need to select File => Trusted Notebook\" align=\"middle\">"
+    "![qiskit_header.png](attachment:qiskit_header.png)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# _*Qiskit Finance: Pricing Fixed-Income Assets*_ \n",
-    "\n",
-    "The latest version of this notebook is available on https://github.com/Qiskit/qiskit-iqx-tutorials.\n",
-    "\n",
-    "***\n",
-    "### Contributors\n",
-    "Stefan Woerner<sup>[1]</sup>, Daniel Egger<sup>[1]</sup>, Shaohan Hu<sup>[1]</sup>, Stephen Wood<sup>[1]</sup>, Marco Pistoia<sup>[1]</sup>\n",
-    "### Affliation\n",
-    "- <sup>[1]</sup>IBMQ"
+    "# Qiskit Finance: Pricing Fixed-Income Assets"
    ]
   },
   {
@@ -42,13 +39,18 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:56:11.814929Z",
+     "start_time": "2019-12-12T17:56:08.147303Z"
+    }
+   },
    "outputs": [],
    "source": [
     "import matplotlib.pyplot as plt\n",
     "%matplotlib inline\n",
     "import numpy as np\n",
-    "from qiskit import BasicAer\n",
+    "from qiskit import Aer\n",
     "from qiskit.aqua.algorithms.single_sample.amplitude_estimation.ae import AmplitudeEstimation\n",
     "from qiskit.aqua.components.uncertainty_models import MultivariateNormalDistribution\n",
     "from qiskit.aqua.components.uncertainty_problems import FixedIncomeExpectedValue"
@@ -57,10 +59,15 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:56:11.854771Z",
+     "start_time": "2019-12-12T17:56:11.850399Z"
+    }
+   },
    "outputs": [],
    "source": [
-    "backend = BasicAer.get_backend('statevector_simulator')"
+    "backend = Aer.get_backend('statevector_simulator')"
    ]
   },
   {
@@ -84,7 +91,12 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:56:11.900271Z",
+     "start_time": "2019-12-12T17:56:11.880164Z"
+    }
+   },
    "outputs": [],
    "source": [
     "# can be used in case a principal component analysis has been done to derive the uncertainty model, ignored in this example.\n",
@@ -107,7 +119,12 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:56:12.650547Z",
+     "start_time": "2019-12-12T17:56:11.936344Z"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -151,7 +168,12 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:56:12.900022Z",
+     "start_time": "2019-12-12T17:56:12.693784Z"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -184,7 +206,12 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:56:12.962157Z",
+     "start_time": "2019-12-12T17:56:12.947977Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -211,7 +238,12 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:56:13.018552Z",
+     "start_time": "2019-12-12T17:56:13.007567Z"
+    }
+   },
    "outputs": [],
    "source": [
     "# specify approximation factor\n",
@@ -224,7 +256,12 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:56:13.058151Z",
+     "start_time": "2019-12-12T17:56:13.051013Z"
+    }
+   },
    "outputs": [],
    "source": [
     "# set number of evaluation qubits (samples)\n",
@@ -237,7 +274,12 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:56:46.568002Z",
+     "start_time": "2019-12-12T17:56:13.098654Z"
+    }
+   },
    "outputs": [],
    "source": [
     "# result = ae.run(quantum_instance=LegacySimulators.get_backend('qasm_simulator'), shots=100)\n",
@@ -247,7 +289,12 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:56:46.598707Z",
+     "start_time": "2019-12-12T17:56:46.592800Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -268,7 +315,12 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:56:46.989666Z",
+     "start_time": "2019-12-12T17:56:46.621212Z"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -321,19 +373,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 12,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2019-08-22T01:57:13.085732Z",
-     "start_time": "2019-08-22T01:57:13.075101Z"
+     "end_time": "2019-12-12T17:56:47.138107Z",
+     "start_time": "2019-12-12T17:56:47.016255Z"
     }
    },
    "outputs": [
     {
      "data": {
       "text/html": [
-       "<h3>Version Information</h3><table><tr><th>Qiskit Software</th><th>Version</th></tr><tr><td>Qiskit</td><td>None</td></tr><tr><td>Terra</td><td>0.9.0</td></tr><tr><td>Aer</td><td>0.3.0</td></tr><tr><td>Ignis</td><td>0.2.0</td></tr><tr><td>Aqua</td><td>0.5.6</td></tr><tr><td>IBM Q Provider</td><td>0.3.2rc1</td></tr><tr><th>System information</th></tr><tr><td>Python</td><td>3.7.4 (default, Aug 13 2019, 15:17:50) \n",
-       "[Clang 4.0.1 (tags/RELEASE_401/final)]</td></tr><tr><td>OS</td><td>Darwin</td></tr><tr><td>CPUs</td><td>4</td></tr><tr><td>Memory (Gb)</td><td>16.0</td></tr><tr><td colspan='2'>Wed Aug 21 21:57:13 2019 EDT</td></tr></table>"
+       "<h3>Version Information</h3><table><tr><th>Qiskit Software</th><th>Version</th></tr><tr><td>Qiskit</td><td>0.14.0</td></tr><tr><td>Terra</td><td>0.11.0</td></tr><tr><td>Aer</td><td>0.3.4</td></tr><tr><td>Ignis</td><td>0.2.0</td></tr><tr><td>Aqua</td><td>0.6.1</td></tr><tr><td>IBM Q Provider</td><td>0.4.4</td></tr><tr><th>System information</th></tr><tr><td>Python</td><td>3.7.5 (default, Oct 25 2019, 10:52:18) \n",
+       "[Clang 4.0.1 (tags/RELEASE_401/final)]</td></tr><tr><td>OS</td><td>Darwin</td></tr><tr><td>CPUs</td><td>4</td></tr><tr><td>Memory (Gb)</td><td>16.0</td></tr><tr><td colspan='2'>Thu Dec 12 12:56:47 2019 EST</td></tr></table>"
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -385,7 +437,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.4"
+   "version": "3.7.5"
   },
   "varInspector": {
    "cols": {
diff --git a/qiskit/advanced/aqua/finance/simulation/.ipynb_checkpoints/option_pricing-checkpoint.ipynb b/qiskit/advanced/aqua/finance/simulation/.ipynb_checkpoints/option_pricing-checkpoint.ipynb
index 93d54bc12..4f7dcc775 100644
--- a/qiskit/advanced/aqua/finance/simulation/.ipynb_checkpoints/option_pricing-checkpoint.ipynb
+++ b/qiskit/advanced/aqua/finance/simulation/.ipynb_checkpoints/option_pricing-checkpoint.ipynb
@@ -1,26 +1,22 @@
 {
  "cells": [
   {
+   "attachments": {
+    "qiskit_header.png": {
+     "image/png": 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"
+    }
+   },
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<img src=\"../../../../../images/qiskit_header.png\" alt=\"Note: In order for images to show up in this jupyter notebook you need to select File => Trusted Notebook\" align=\"middle\">"
+    "![qiskit_header.png](attachment:qiskit_header.png)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# _*Qiskit Finance: Option Pricing*_ \n",
-    "\n",
-    "The latest version of this notebook is available on https://github.com/Qiskit/qiskit-iqx-tutorials.\n",
-    "\n",
-    "***\n",
-    "### Contributors\n",
-    "Stefan Woerner<sup>[1]</sup>, Daniel Egger<sup>[1]</sup>, Christa Zoufal<sup>[1]</sup>, Shaohan Hu<sup>[1]</sup>, Stephen Wood<sup>[1]</sup>, Marco Pistoia<sup>[1]</sup>\n",
-    "\n",
-    "### Affliation\n",
-    "- <sup>[1]</sup>IBMQ"
+    "# Qiskit Finance: Option Pricing"
    ]
   },
   {
@@ -51,7 +47,7 @@
     "- <a href=\"basket_option_pricing.ipynb\">Basket Option</a> (multivariate, payoff with 2 segments)\n",
     "- <a href=\"asian_barrier_spread_pricing.ipynb\">Asian Barrier Spread</a> (multivariate, path-dependent, payoff with 3 segments)\n",
     "\n",
-    "More examples on option pricing with a quantum computer can be found in the [Qiskit Finance Community](https://github.com/Qiskit/qiskit-iqx-tutorials-community/tree/master/finance) section of the Qiskit Community Tutorials.\n",
+    "More examples on option pricing with a quantum computer can be found in the [Qiskit Finance Community](https://github.com/Qiskit/qiskit-community-tutorials/tree/master/finance) section of the Qiskit Community Tutorials.\n",
     "\n",
     "All examples illustrate how to use the generic Qiskit Finance framework to construct QAE-operators (uncertainty problems). The same framework can be easily adjusted to estimate risk as well, for instance, the Value at Risk (VaR) or the Conditional Value at Risk (CVaR, also known as Expected Shortfall). How to use Qiskit Finance for risk analysis is illustrated in the following tutorial:\n",
     "<a href=\"credit_risk_analysis.ipynb\">Credit Risk Analysis</a>\n",
@@ -64,19 +60,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2019-08-22T01:57:32.797978Z",
-     "start_time": "2019-08-22T01:57:32.787666Z"
+     "end_time": "2019-12-12T17:57:42.903632Z",
+     "start_time": "2019-12-12T17:57:40.319538Z"
     }
    },
    "outputs": [
     {
      "data": {
       "text/html": [
-       "<h3>Version Information</h3><table><tr><th>Qiskit Software</th><th>Version</th></tr><tr><td>Qiskit</td><td>None</td></tr><tr><td>Terra</td><td>0.9.0</td></tr><tr><td>Aer</td><td>0.3.0</td></tr><tr><td>Ignis</td><td>0.2.0</td></tr><tr><td>Aqua</td><td>0.5.6</td></tr><tr><td>IBM Q Provider</td><td>0.3.2rc1</td></tr><tr><th>System information</th></tr><tr><td>Python</td><td>3.7.4 (default, Aug 13 2019, 15:17:50) \n",
-       "[Clang 4.0.1 (tags/RELEASE_401/final)]</td></tr><tr><td>OS</td><td>Darwin</td></tr><tr><td>CPUs</td><td>4</td></tr><tr><td>Memory (Gb)</td><td>16.0</td></tr><tr><td colspan='2'>Wed Aug 21 21:57:32 2019 EDT</td></tr></table>"
+       "<h3>Version Information</h3><table><tr><th>Qiskit Software</th><th>Version</th></tr><tr><td>Qiskit</td><td>0.14.0</td></tr><tr><td>Terra</td><td>0.11.0</td></tr><tr><td>Aer</td><td>0.3.4</td></tr><tr><td>Ignis</td><td>0.2.0</td></tr><tr><td>Aqua</td><td>0.6.1</td></tr><tr><td>IBM Q Provider</td><td>0.4.4</td></tr><tr><th>System information</th></tr><tr><td>Python</td><td>3.7.5 (default, Oct 25 2019, 10:52:18) \n",
+       "[Clang 4.0.1 (tags/RELEASE_401/final)]</td></tr><tr><td>OS</td><td>Darwin</td></tr><tr><td>CPUs</td><td>4</td></tr><tr><td>Memory (Gb)</td><td>16.0</td></tr><tr><td colspan='2'>Thu Dec 12 12:57:42 2019 EST</td></tr></table>"
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -128,7 +124,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.4"
+   "version": "3.7.5"
   },
   "varInspector": {
    "cols": {
diff --git a/qiskit/advanced/aqua/finance/simulation/asian_barrier_spread_pricing.ipynb b/qiskit/advanced/aqua/finance/simulation/asian_barrier_spread_pricing.ipynb
index 1ce496781..18c4953b8 100644
--- a/qiskit/advanced/aqua/finance/simulation/asian_barrier_spread_pricing.ipynb
+++ b/qiskit/advanced/aqua/finance/simulation/asian_barrier_spread_pricing.ipynb
@@ -1,25 +1,22 @@
 {
  "cells": [
   {
+   "attachments": {
+    "qiskit_header.png": {
+     "image/png": 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"
+    }
+   },
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<img src=\"../../../../../images/qiskit_header.png\" alt=\"Note: In order for images to show up in this jupyter notebook you need to select File => Trusted Notebook\" align=\"middle\">"
+    "![qiskit_header.png](attachment:qiskit_header.png)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# _*Qiskit Finance: Pricing Asian Barrier Spreads*_ \n",
-    "\n",
-    "The latest version of this notebook is available on https://github.com/Qiskit/qiskit-iqx-tutorials.\n",
-    "\n",
-    "***\n",
-    "### Contributors\n",
-    "Stefan Woerner<sup>[1]</sup>, Daniel Egger<sup>[1]</sup>\n",
-    "### Affliation\n",
-    "- <sup>[1]</sup>IBMQ"
+    "# Qiskit Finance: Pricing Asian Barrier Spreads"
    ]
   },
   {
@@ -60,8 +57,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
+   "execution_count": 15,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:45:49.593103Z",
+     "start_time": "2019-12-12T17:45:49.585361Z"
+    }
+   },
    "outputs": [],
    "source": [
     "import matplotlib.pyplot as plt\n",
@@ -70,7 +72,7 @@
     "%matplotlib inline\n",
     "import numpy as np\n",
     "\n",
-    "from qiskit import QuantumRegister, QuantumCircuit, BasicAer, execute\n",
+    "from qiskit import QuantumRegister, QuantumCircuit, Aer, execute\n",
     "from qiskit.aqua.algorithms import AmplitudeEstimation\n",
     "from qiskit.aqua.circuits import WeightedSumOperator, FixedValueComparator as Comparator\n",
     "from qiskit.aqua.components.uncertainty_problems import UnivariatePiecewiseLinearObjective as PwlObjective\n",
@@ -98,8 +100,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
+   "execution_count": 16,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:45:52.003138Z",
+     "start_time": "2019-12-12T17:45:51.989434Z"
+    }
+   },
    "outputs": [],
    "source": [
     "# number of qubits per dimension to represent the uncertainty \n",
@@ -137,8 +144,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
+   "execution_count": 17,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:45:54.248971Z",
+     "start_time": "2019-12-12T17:45:54.034528Z"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -211,8 +223,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
+   "execution_count": 18,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:45:56.620724Z",
+     "start_time": "2019-12-12T17:45:56.616363Z"
+    }
+   },
    "outputs": [],
    "source": [
     "# determine number of qubits required to represent total loss\n",
@@ -228,8 +245,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
+   "execution_count": 19,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:45:58.955640Z",
+     "start_time": "2019-12-12T17:45:58.947995Z"
+    }
+   },
    "outputs": [],
    "source": [
     "# set the strike price (should be within the low and the high value of the uncertainty)\n",
@@ -251,8 +273,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
+   "execution_count": 20,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:46:01.014063Z",
+     "start_time": "2019-12-12T17:46:01.009271Z"
+    }
+   },
    "outputs": [],
    "source": [
     "# condition and condition result\n",
@@ -265,8 +292,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
+   "execution_count": 21,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:46:03.264901Z",
+     "start_time": "2019-12-12T17:46:03.257104Z"
+    }
+   },
    "outputs": [],
    "source": [
     "# set the approximation scaling for the payoff function\n",
@@ -296,8 +328,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
+   "execution_count": 22,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:46:05.942721Z",
+     "start_time": "2019-12-12T17:46:05.485458Z"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -349,8 +386,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
+   "execution_count": 23,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:46:07.880221Z",
+     "start_time": "2019-12-12T17:46:07.872557Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -380,8 +422,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
+   "execution_count": 24,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:46:13.137268Z",
+     "start_time": "2019-12-12T17:46:12.829426Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -409,17 +456,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
+   "execution_count": 25,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:46:17.597554Z",
+     "start_time": "2019-12-12T17:46:16.757441Z"
+    }
+   },
    "outputs": [],
    "source": [
-    "job = execute(qc, backend=BasicAer.get_backend('statevector_simulator'))"
+    "job = execute(qc, backend=Aer.get_backend('statevector_simulator'))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
+   "execution_count": 26,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:46:20.539775Z",
+     "start_time": "2019-12-12T17:46:20.256459Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -460,8 +517,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
+   "execution_count": 27,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:46:22.567327Z",
+     "start_time": "2019-12-12T17:46:22.563234Z"
+    }
+   },
    "outputs": [],
    "source": [
     "# set number of evaluation qubits (=log(samples))\n",
@@ -473,8 +535,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
+   "execution_count": 28,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:56:19.718954Z",
+     "start_time": "2019-12-12T17:46:24.741251Z"
+    }
+   },
    "outputs": [],
    "source": [
     "# result = ae.run(quantum_instance=BasicAer.get_backend('qasm_simulator'), shots=100)\n",
@@ -483,8 +550,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
+   "execution_count": 29,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:56:22.568673Z",
+     "start_time": "2019-12-12T17:56:22.561336Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -492,7 +564,7 @@
      "text": [
       "Exact value:    \t0.8023\n",
       "Estimated value:\t0.5000\n",
-      "Probability:    \t0.6958\n"
+      "Probability:    \t0.2694\n"
      ]
     }
    ],
@@ -504,12 +576,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
+   "execution_count": 30,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:56:26.099389Z",
+     "start_time": "2019-12-12T17:56:25.788259Z"
+    }
+   },
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -521,7 +598,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -558,19 +635,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 31,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2019-08-22T01:55:10.230176Z",
-     "start_time": "2019-08-22T01:55:10.222004Z"
+     "end_time": "2019-12-12T17:56:29.135968Z",
+     "start_time": "2019-12-12T17:56:29.021462Z"
     }
    },
    "outputs": [
     {
      "data": {
       "text/html": [
-       "<h3>Version Information</h3><table><tr><th>Qiskit Software</th><th>Version</th></tr><tr><td>Qiskit</td><td>None</td></tr><tr><td>Terra</td><td>0.9.0</td></tr><tr><td>Aer</td><td>0.3.0</td></tr><tr><td>Ignis</td><td>0.2.0</td></tr><tr><td>Aqua</td><td>0.5.6</td></tr><tr><td>IBM Q Provider</td><td>0.3.2rc1</td></tr><tr><th>System information</th></tr><tr><td>Python</td><td>3.7.4 (default, Aug 13 2019, 15:17:50) \n",
-       "[Clang 4.0.1 (tags/RELEASE_401/final)]</td></tr><tr><td>OS</td><td>Darwin</td></tr><tr><td>CPUs</td><td>4</td></tr><tr><td>Memory (Gb)</td><td>16.0</td></tr><tr><td colspan='2'>Wed Aug 21 21:55:10 2019 EDT</td></tr></table>"
+       "<h3>Version Information</h3><table><tr><th>Qiskit Software</th><th>Version</th></tr><tr><td>Qiskit</td><td>0.14.0</td></tr><tr><td>Terra</td><td>0.11.0</td></tr><tr><td>Aer</td><td>0.3.4</td></tr><tr><td>Ignis</td><td>0.2.0</td></tr><tr><td>Aqua</td><td>0.6.1</td></tr><tr><td>IBM Q Provider</td><td>0.4.4</td></tr><tr><th>System information</th></tr><tr><td>Python</td><td>3.7.5 (default, Oct 25 2019, 10:52:18) \n",
+       "[Clang 4.0.1 (tags/RELEASE_401/final)]</td></tr><tr><td>OS</td><td>Darwin</td></tr><tr><td>CPUs</td><td>4</td></tr><tr><td>Memory (Gb)</td><td>16.0</td></tr><tr><td colspan='2'>Thu Dec 12 12:56:29 2019 EST</td></tr></table>"
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -622,7 +699,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.4"
+   "version": "3.7.5"
   },
   "varInspector": {
    "cols": {
diff --git a/qiskit/advanced/aqua/finance/simulation/basket_option_pricing.ipynb b/qiskit/advanced/aqua/finance/simulation/basket_option_pricing.ipynb
index 1b62851eb..5d9a187b4 100644
--- a/qiskit/advanced/aqua/finance/simulation/basket_option_pricing.ipynb
+++ b/qiskit/advanced/aqua/finance/simulation/basket_option_pricing.ipynb
@@ -1,25 +1,22 @@
 {
  "cells": [
   {
+   "attachments": {
+    "qiskit_header.png": {
+     "image/png": 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"
+    }
+   },
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<img src=\"../../../../../images/qiskit_header.png\" alt=\"Note: In order for images to show up in this jupyter notebook you need to select File => Trusted Notebook\" align=\"middle\">"
+    "![qiskit_header.png](attachment:qiskit_header.png)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# _*Qiskit Finance: Pricing Basket Options*_ \n",
-    "\n",
-    "The latest version of this notebook is available on https://github.com/Qiskit/qiskit-iqx-tutorials.\n",
-    "\n",
-    "***\n",
-    "### Contributors\n",
-    "Stefan Woerner<sup>[1]</sup>, Daniel Egger<sup>[1]</sup>\n",
-    "### Affliation\n",
-    "- <sup>[1]</sup>IBMQ"
+    "# Qiskit Finance: Pricing Basket Options"
    ]
   },
   {
@@ -47,8 +44,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
+   "execution_count": 2,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:48:15.091384Z",
+     "start_time": "2019-12-12T17:48:15.074739Z"
+    }
+   },
    "outputs": [],
    "source": [
     "import matplotlib.pyplot as plt\n",
@@ -57,7 +59,7 @@
     "%matplotlib inline\n",
     "import numpy as np\n",
     "\n",
-    "from qiskit import BasicAer, QuantumRegister, QuantumCircuit, execute\n",
+    "from qiskit import Aer, QuantumRegister, QuantumCircuit, execute\n",
     "from qiskit.aqua.algorithms import AmplitudeEstimation\n",
     "from qiskit.aqua.components.uncertainty_problems import MultivariateProblem\n",
     "from qiskit.aqua.components.uncertainty_problems import UnivariatePiecewiseLinearObjective as PwlObjective\n",
@@ -85,8 +87,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
+   "execution_count": 3,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:48:16.343149Z",
+     "start_time": "2019-12-12T17:48:16.322565Z"
+    }
+   },
    "outputs": [],
    "source": [
     "# number of qubits per dimension to represent the uncertainty \n",
@@ -124,8 +131,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
+   "execution_count": 4,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:48:17.743717Z",
+     "start_time": "2019-12-12T17:48:17.158077Z"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -192,8 +204,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
+   "execution_count": 5,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:48:18.274595Z",
+     "start_time": "2019-12-12T17:48:18.267991Z"
+    }
+   },
    "outputs": [],
    "source": [
     "# determine number of qubits required to represent total loss\n",
@@ -209,8 +226,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
+   "execution_count": 6,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:48:18.946439Z",
+     "start_time": "2019-12-12T17:48:18.935294Z"
+    }
+   },
    "outputs": [],
    "source": [
     "# set the strike price (should be within the low and the high value of the uncertainty)\n",
@@ -249,8 +271,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
+   "execution_count": 7,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:48:20.314739Z",
+     "start_time": "2019-12-12T17:48:20.100968Z"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -281,8 +308,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
+   "execution_count": 8,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:48:20.914716Z",
+     "start_time": "2019-12-12T17:48:20.908251Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -310,8 +342,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
+   "execution_count": 10,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:48:31.104508Z",
+     "start_time": "2019-12-12T17:48:30.970506Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -339,17 +376,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
+   "execution_count": 11,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:48:32.350280Z",
+     "start_time": "2019-12-12T17:48:32.042056Z"
+    }
+   },
    "outputs": [],
    "source": [
-    "job = execute(qc, backend=BasicAer.get_backend('statevector_simulator'))"
+    "job = execute(qc, backend=Aer.get_backend('statevector_simulator'))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
+   "execution_count": 12,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:48:33.425816Z",
+     "start_time": "2019-12-12T17:48:33.403644Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -386,8 +433,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
+   "execution_count": 13,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:48:34.420915Z",
+     "start_time": "2019-12-12T17:48:34.415416Z"
+    }
+   },
    "outputs": [],
    "source": [
     "# set number of evaluation qubits (=log(samples))\n",
@@ -399,8 +451,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 14,
    "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:51:40.049915Z",
+     "start_time": "2019-12-12T17:48:35.268300Z"
+    },
     "scrolled": true
    },
    "outputs": [],
@@ -411,8 +467,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
+   "execution_count": 15,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:51:41.113659Z",
+     "start_time": "2019-12-12T17:51:41.102390Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -432,8 +493,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
+   "execution_count": 16,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:51:42.566087Z",
+     "start_time": "2019-12-12T17:51:42.156782Z"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -486,19 +552,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 17,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2019-08-22T01:55:31.656034Z",
-     "start_time": "2019-08-22T01:55:31.646368Z"
+     "end_time": "2019-12-12T17:51:43.795082Z",
+     "start_time": "2019-12-12T17:51:43.656955Z"
     }
    },
    "outputs": [
     {
      "data": {
       "text/html": [
-       "<h3>Version Information</h3><table><tr><th>Qiskit Software</th><th>Version</th></tr><tr><td>Qiskit</td><td>None</td></tr><tr><td>Terra</td><td>0.9.0</td></tr><tr><td>Aer</td><td>0.3.0</td></tr><tr><td>Ignis</td><td>0.2.0</td></tr><tr><td>Aqua</td><td>0.5.6</td></tr><tr><td>IBM Q Provider</td><td>0.3.2rc1</td></tr><tr><th>System information</th></tr><tr><td>Python</td><td>3.7.4 (default, Aug 13 2019, 15:17:50) \n",
-       "[Clang 4.0.1 (tags/RELEASE_401/final)]</td></tr><tr><td>OS</td><td>Darwin</td></tr><tr><td>CPUs</td><td>4</td></tr><tr><td>Memory (Gb)</td><td>16.0</td></tr><tr><td colspan='2'>Wed Aug 21 21:55:31 2019 EDT</td></tr></table>"
+       "<h3>Version Information</h3><table><tr><th>Qiskit Software</th><th>Version</th></tr><tr><td>Qiskit</td><td>0.14.0</td></tr><tr><td>Terra</td><td>0.11.0</td></tr><tr><td>Aer</td><td>0.3.4</td></tr><tr><td>Ignis</td><td>0.2.0</td></tr><tr><td>Aqua</td><td>0.6.1</td></tr><tr><td>IBM Q Provider</td><td>0.4.4</td></tr><tr><th>System information</th></tr><tr><td>Python</td><td>3.7.5 (default, Oct 25 2019, 10:52:18) \n",
+       "[Clang 4.0.1 (tags/RELEASE_401/final)]</td></tr><tr><td>OS</td><td>Darwin</td></tr><tr><td>CPUs</td><td>4</td></tr><tr><td>Memory (Gb)</td><td>16.0</td></tr><tr><td colspan='2'>Thu Dec 12 12:51:43 2019 EST</td></tr></table>"
       ],
       "text/plain": [
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@@ -550,7 +616,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.4"
+   "version": "3.7.5"
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diff --git a/qiskit/advanced/aqua/finance/simulation/bull_spread_pricing.ipynb b/qiskit/advanced/aqua/finance/simulation/bull_spread_pricing.ipynb
index fa23b5eef..5b14e33dc 100644
--- a/qiskit/advanced/aqua/finance/simulation/bull_spread_pricing.ipynb
+++ b/qiskit/advanced/aqua/finance/simulation/bull_spread_pricing.ipynb
@@ -1,25 +1,22 @@
 {
  "cells": [
   {
+   "attachments": {
+    "qiskit_header.png": {
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K7KdEZsu7QvYk/wuyZbahTeNE7Q/YTF8+gXY5+vSriPjkPZAYM/2yHpv9pM2pQ5svlCDWSeTlgxgzkXdQVLYP0OGiDNpS7mHR5m44iMn2bRZ5x+Xg0dmSbMPuI8d60RtCf8Fm1tofM8FFvh+YEM1nYkBKspxfaq3Y7HgsML9bkPMsFsT6C3kjQIzxyDsnJttkdKgOg3ZNNCHidR2IyXasGXnlavDobEm28iIWOYYSW8Fl2MJcvW8QBS5JOIuIL1aBdNR5ZTVmbLE/hgKYqUcO+54CRLqPvFQQIxt518Vk+zY6/AAdyLfgZT8Qk+3gJuTd8gPPzpZkqz5iL+JBOHD8t5kc4Vb9OFYDLvCe04RovZwEEgmd83utI1r9Fh1wAm6yyDmrBZFeSZLtYzHZLkGHUuhI4RkfEJPtgBrkPQoFz86WZEsl1iLH+ElLg4X8/clUe2nJABq6FPcbItbukIMEvIfuuNNkwVa3Z7SEusVoTySTAnEskmRbLyJbYXjDpTrdIuIp8qriwMOzJdkqStGmfjg0U/T72oCtrPrKX4tju0qFKahGxOvDoLs0mesv1hpZbFX3aT8FNMuoRpuNDIjijZJki7SIbA+gQ6RE2QZUIK8+FTw9W5Ktz2l7n5fU/PdMuog8s/7Jb+8mqaEzfb9GxIbO1nm97KAzutFbymsNVnQwnRingVbMSQtyrvqDKOESZesnIttf0WG0NNmqypFnGA6eni3JlkqpRY5hLfDogOK/UcBseHVh5+S+jHOIArLRTxGxYmLn1ToDIW3avLKKemGzaD47zY8G3vsGe8pDaRAjQaJso0Vk+xk6LJEm2/3Is0wCj8+WZKtYhDav0kBIETT7vAUFrMbGJye3FQzwea3BPnZeoTtt6Zf5dZVt89V2wH3LFi8dkvXe11erDSYWBeoP5vvLQah/JdqsYcS1J1G2qSKyXYMOP0qS7VIUmAMkW5Kt9xF7lReU4EyunfBzDTqxGJpe/v398vGxPv/5Tx8bPkQq6w4iPpwF7XOq3BH7fxQhg2fvPH6/1mC2opNHe4ZpZeCMPmpBznkNiDFcomwzRGQ7Hx0aEyXIdpwVne7fJFuSbUwlcgzroQ3aJ3X1X0Z0xpr1jVVXftsya2hfb8rLzl5iwCoroulYaFfZetlRqpCMgmXfnv2n1mC0sOis+khxtIoGAeEouS6FAhFSJcq2n4hshyDvH/9uZxtf99peSZItyTbfXk39MGgP4z9qx3UTvo61GAx1z68f+WL1rJyUcB/aNtZNvICIzxZBJ7jLvEMTRsxY9slvfz2u1evNFmyj+vfSFI0C2pNcgzbz5CBCjETZhorIFp4h71xMN7PVViDvayDZkmxb/5ewN7XQLkrGhI/bfKkO28GaTQZ9Y82jmxd//XLr0tKfWETLpTRfH0ZBQTOaYbx9A0P7xqWOnFT0wYZPDpy+8ehVg95gMrPYnsc/l2QEKGQUtEt9yYqc75UgQqBE2fqIyXYPClj2xXcnW+oo8o7SJFsOyZa+jBzTPmiLVqo1Go2PWqkKHvzuj3easAOs1WwyGgwm5BgfXf/70oVTx+1OnLl06a8bFQ+f19Q3NDUZjEazhcWOvDq3dXKcVqlS+2i0GrVS5gVt7DIi504QiKCQJlsriMk2A51Yz64fpXE32y0ocEVGsuWQbCNqkKOfC69hvAN0QUGBfhqtj1qtZOTqiGEL912vwy6xVqvV0srKYbEr5qendxQm+zMKRqX29tFo/AKCgoICNAwFziY3IccwmAIRDJJkWycqW/gOX2e9+tWHeTG06Gyno5PlJFsOyXacvaraJBDwUgWERUZGhAUHBQZw3Wq8vVUMw8hpRpcybdPhe3UoHePzP75amBOjkdMKhlHaotVqfAMCg4LDIiL7hgeqKBCIeoY24o6UqpQk2yfisg2qwXYZrn6xIIMRkW2qHp0Y+pFsSbYAq5pvBVq+WSYwOr5fbEwU160uKMBfq9VqfLzVKiVD0wAyRh2YMmn5vvNPGljsFv2La79tmzs8WssoZF5AyRS28bGtWj//wCBdWEREVGxs//gYnQocmAtW5HzCgAh3kZcNYoxC3i1x2cI0FjtkOv1usIvZ6h7ja/6gSLYkW/gNOeYfoQWliUpKSU4cOCA2NioiPITrNsDfV+utUshoaN3ZRMkYpXd4+qSSHT9fvFtjFJmvqfaf8oMfL56aHe2vZmQ0/wYikClUaq3WPyAgSBcabq92YEJyalIsv1fqEyNyLmpBhD+RNxHEmI68SyKzhXksdoK9UEi7kG15ObbxAcmWZKu6KzzTXB6WnDlkUHpaStJA7n7bNzwiXBekVStkjmIFKJmcUWlC4gZNmLNs+76jf9x6WCncUCxkNdS9eHznr9/3f7Rm3qTMfmFaNWMLludol5LJ1dogXTh3r42J7TcwISUtfVBmZkq4HOzm6pHzrC+IcBR5c0CMEuT9JDZbeNeKnaqYTnWRbfuaokm2Hp9tTANymsbZo43MzMkZMSxrSHpacuKAeO5+GxqgllPgXGx7+dpmpb5hcYkZ2bmTp89774OV6+3WLil5r3hG/ujsQUlxEf4+ahXDyPlc22cbL6v9Q6Ni4/rbqx2SNXRETm5mtAI4Q2qRY8miwXUbkbfY7cPjVorOFrLvY+fKw93JFk+TbD0+29EscirjAGR9c96aOG5U7sgRQ7luU1MGRgWqbLdZEIGiKa8+IJMpmBYKhVwmk9EUBWJ4Aa0K7DswOTU9Y0j2iJG5o8ZNnJwTLQMIe8gip0gOrstH3mYQYw/y8sRnC6rdJuxU1UgXs31exCJvHsnW07MtQQ573RvCxr1dWJD/1oRxuVy4wwYPCPGhwQvE8qLkcpWPigK38eV6B/fPGMZFmzs2b1J+QeHMcZFAN69JbWDAdVHI+wrE2I+8cBHZ8kI312BnLONdyrZ6IOxFXl04ydbDs92DHPMvqhHvvlNcNLNwSv7EvLwxWXEad8OjlT6+umA1SMM7asjovLwJ+QUzZs4ufufdXNWXJuTsV4EIgnJugBj/CMIBUdny1IV772DH6vu7kG1DBoDvC+QdIdl6eLZH7dmWTV9aurBkfnHR2zOmT84MlYP7VIFhUf3DFCARefCgidNnvF1UvKBkYenSwr1G5JR7gwin0IENcPMd7E+Jz5YXMHFbuQnbd1/ZZbaGkcCZhgKFJFuPzlZxFTmW31esXLZk8fvvLJg/MzMIukUWHJ80ZFAISMcvY/q8+e8uLF2ybOWKg2bkPAoGEbYi7y1wXSHydriXLU+VPX/3iccstlHaVbbmCW2fT1KtI9l6crb+j5Fj/nX9hrWrlixdWpIbBN1E+aUNHf3WEA1IyDe7ZOnS5avXbVi/39wy1xNhKvJ2g+v2Iu9t97MV8h5Ssq8anVR5d54tW9je08B+Itl6crYRjc2D5O3bNq/fuG5qGHSfKnnitOLiZBqkFFKwZtOmLdt3fG9EjjGTcnNN6rp7ByyzkdJkayMbdRaFFnSebQm0WoIC+SRbD842iUWOoezT3bs+WplGgQTo8JnvLds0LwKklbzs448++eyHRrQZR7v7XIIMt47FuAASZMvLb0TewU6zXQEO8uvIq/Qj2Xputjlo0/jL119/+24gSEPz9sadX32TrwBp+RV/8+2+n2rRplDu7m6nMrd2V82XNlsYjrx6eSfZbgOBwVbk7SPZem62+WhTf/jnsqlykAg9as+Bo5e/HQQSoyft/+VwDdosVIAIASZ0sEaDawaw6GDylzhb+BZ5KR1newycfIwCY0m2HpvtbLSpO3V0KkgnduOl68+q1mpBahOPnmrOdjkDYvyKvI/ANV8h7yBInW1QnbA/V5+4p32OvCcakq2nZvse2jSULwAJyd6+2mDGCzkgudmXatFmKyMud+Tp48AViUbkTZE8WziBDjNdzhbyUeBzkq2nZrscbZq+VIKU+h2w9bHZD6Qm/6gRbbaJy1b+CnlXFNA15S3k1Sulz1bQZqnr2cIh5LHDSbYemu0ytKkZBpKiZt5HxL/zQHKpL9DmS6XoSHjboWufosA2kD7bYnSYLyLbiEbkPVCRbD04W+sVNUgr7HMzonlPMEiNOW9Fzrcis9XVIo8dJWpUjZUad7IdUa7renZiN0lEtrAIBXaSbD0z2+3IsRwGqb11DRFvTwHJlZndyRZKUKBuOHRuTCMKzAI3si0y4TVf6Ng+dBgsJlv6CvKsg0m2HpntOnu25yiQWOAOA6L16yiQ2jGLW9nSV1HAOAU6U2RGgXJwI9sNXb3Hw210iBKTLaRZkHebIdl6YrZL0eZFAkht1CVEfDSbBmnFPEObr5Ug0mAWBdj3oWMrUIjNEJ8tcwDtTjHQAa0VW9XIRGULu1BgA8nWc5ekTJ+B1DTrGhFxf3+Q1sdGtNnOgFhfopNjA6B9aWfQydcgOtvAS9jiRiK07wd02AfisvV5jDxzMsnWA7NdgnYNM0Bq2acR8XmJHKQ0vRHttonPNuAlOrHs1UFbkd+z6KTCT3S28Q/QwbiYgnaUIm+yyGxhAgpclbmT7TmSba/Odh42q8wDiSmX1iDi4XSQ0LhKbLaaAdHSG9BZw7cFGueyCw8Y0FllXxCbLV2BQmdioI08CzoYvMVmC7+gwAp3sv2CZNursy3EFlVTQWJphxCxZokSJFP0ClssUoB4Iwz4OtPJJTNyEoIUusTcwpUXrPi6hhQQnS0k1qAQe6pQCUK5Z1FgG4jONrQeecb+bmRbig5ly3lToFcg2eZhK/1aNUhK/n4lIp7MBolodxmx1Sw5uGGSBcUxjwI3soX0enRW9/nUrEg5AKjic+ddRqHnPuKzhRIUKKfEZzse23UMegWS7RB0sJzMAEkNKEPExnUakERuuRUd8mTgjtkozixwK1vIbsK22Mqbr7CNQnAjW6ocBRaJzzaWZNurs40zIa9mUyhIiJrzEBEv5YIE4j83IM+cTYNbSszoutrJ4Ga2kGtE15SBO9lCkhl5TTGis4U/Sba9OdvgKhRg7y0KAulEfW1FNGwPgO6KWP2CdQoqiQL3ZNxDV/3ZF9zOFka/RFcclLuXLWxFgTPis023kmx7cbbeFejEWvFhOEhm2h1EvDYRumfgtqdWdPI4HNyl3ouu2aWAbmQLuoPYtRMMuJmt6iEKzBedLXxOsu3F2cJ55FiuPcZW7LM9GTRII+QTM6L5szBwnzp33ysWW909bUbOVR9w38SX2LUXEwDcypY3ux47x+5WgrvZwlgUqA8Xna1/Jcm2F2f7A3JMXw44jjzjqbkRIIm8K/ZHy4GbqITlf5mQtz96txE5B1XQDbrttdi5+wuU0O1sIfJs579INoD72cJ+597EZgtBB0i2vTfbVcixnAPlgofIYyt/nBIM3ee/1YDI7osGN8jiSk7Uo8C1KTI4ZUHODga6RTX3GnasPJ8CkCBboAo7/mVerFdBt7LV1aLATJHZ2kx4RrLtrdlOsVf6UAcQuv6RkRWU+7xs3gAfRgbdMvwci/hkgRzEoBTK4BFrL9Sz6GA13FjkDxB4l0XOAgV0V3ZZI7bDenVPNvDczJaX83MDtuPMFAVA97KFeShQoxOfLWjyFn95sZpk2wuzTTMhp3aIrZb47Q8NVhR4eXptbpiKocFt3itqEPFgshfHxWQZbdrcb+6YUMBiuF4aApykantbOTR0H9VvxvYztcgznFk/SgNSU4zcfqYSeYY/dk2PBHcRJFt+aUI/G+xiV9/Sm1HI8vC3Zbl91YycgnZ5CUFbg46yiFWlSq/XQVsyRhmUPnfvX3UsCrCmpgslgWA3XY+cV3EgmfHo8Dn0GN+M0flFJXMmj0yNlgPRXSRb+hxyjLuhRdCsYzUGKzqxPP5964xUHdcuLYiV+6yPF6cPp+MUmUVViHgik7vOcWGfNvVSNKP07TduedmdJnRmMTzb/5YGWmw1IudvX5BMqjDb3oEg2cKn9jjOyPmQ0zdcaTSy+JrGO4e3zMyO0KpsN94+LcV6dd1t4k8sYu0aHz7bPvxroQ/QCqVPcMpby74rr2axTbOvTi/qJ4NW9DELcvYpSbaejWQ7C22eRICAduyntxqNVmzD8Kz8+7Uzh/ULUMplMrCV2GW2suLHiHguB/ibLfeK//TxomkZ4xc7ZMqyL049qLPi61iz4eXZ5RkMCAQ/spe9UEGy9Wwk20QDcprywFnA6J3ltQYzi+2wvrxz5uuVc8alxgR6q5QKhZymKQBhtEJx+6yI+i0BtqopSqZQMEq1Ljpl7OxVe4/feG7E9liM+keHPkhlwFluA3LMOTTJ1rORbL0fIMewCdpQp5X+9qDROV0h1vjy9sXDX20qnTVpZHq/0CA/jVqlUioZjqIVwzDKwgpEvDItKCw2dVhe0aJNXx2+eLtKz2IHrEb9y4t7Cvq20+YqA3IehYNnZ0uQbGF/84YLBbSDCh21+sj9JoPZip0yNdVUPrh26fTBn775YufmDcsXlTb7cO2Wzbt/eWyLsfJ+ZXWDkcXOsGajvrL805mJKmiP/IQFOYdUnp4tQbItQZsX/aEdXgC0f+KEVfuvVjYZTFYWe4zVYtRXVxzbNTc7xp+hwAvaEfUMbZYqQDopJFuiN2aboUeOfh60RfmERPYN1/lptCFp+at+KH9arzeYLdLGy3LBGhpf3jqya/bQ2OCAwLCo2Pi4MC0NbRXqkWPMpkE6gSRbojdm6/83csxl8Dp5YMzA/rHREeGhQUH+PoxCGRibW7y57OLD2kaD0WTp5r2XZS1cr031T/48uHvhxJQwrcrHLygkIjI6Nn5AQkpy/xAlBa/53oScm0EgpVfY6tJCgfFAEG9wtswOe0VPIkGIUoUnpackDIyLjYmMCNUFBfhrtSoF9PECJiRpwvvb9l+4/ayuUW/rV0zArNViMRkN+sa6Z7cv/LxncUF6hDcFXn1opdYvKCg0PCIqNm5AQnJa+uCsjBgfGoR091nkfMyAlC5gu34BgniDs6XHWpCjLwIBdezgYZkZ6SmJAwfExUaFhwUHaFVyCgRobUzGmKLlu/YdvnDjEbfa1NikNxi4ik1ms4VnNptNHINB36RvaqitfHLj/KF9O5cVjcmI8ZWDEC1TB4RExtqqTUrPGJI1dGTuiIHewnALmuzhT5SBlPaSbIlemC2EVCDH/AufiDxq5LjcnGFZQzLSkxMGxMWE+avl0CHGJyg6eei4KUULV2ze8cXXPx86cupcizPHDh0q+2bvrk0r3p8zNW9EalywDwMd8pKrdVEDElPSMzKzR+bmjs2bPLafGhx+MCPnQRhIaibJluiN2TIfo83zeGgmC86ZUjBxbG7uyKFZg9KT40PUNIhDyVvQjiL/83//BzLaC7oi84lIGJw9PCd37PhJBQUzZ02IlEOzqCcscvYqQVLUnyRbohdmS482IcewBOyY1KLimVMLJueNG507YnC/QAVIwItWqDUB/gy4QBGcOHzM+An5BdNnFs0rmT/Uuzn2EgNyLJNkIK00K8mW6H3ZQuA15FjLfWx9+U4sfX/+vKJCLtwJI/trQCKU0i8oNEonB5doE0blz+CiXfDeBx8unxpKA4DyrAU5t4NBap+QbIlemC2zFh37kkPmrl7y4aL35hXPmTkp0Rsk48XoouISEwPBRT4Jk4sXvL/ow6UrV298P4oGGFGHNlsYkBqzwUSyJXpdtlRKLXLMZTSEfLB1w9pVS5eULpybqQUpyYIGpAwZmaoAV2mGFH+4YvXajZu37l4RDdRX9rYahlAgvcRyki3R27IF1c9oU5WsWfzZ7u1bNm9Yt7wgDCTmmzRibH5BMLguZPLqzVt37P7k86/W+cU+RZsTPtATqGnbDlaYSLZEb8pWNsmMHOMnM3/86vPPPtmza02WHKQm7zehsPiDMWK6k/8/e/fh0NS993H8H3nuvf1mgmAAARdOcNGiRdy7tigWtO7Zwa3bqrUW9x5VXGC0roqrqFVcOCpOlkzDDPOQ9XnOyCEhhhDUXPTe8+o+g5P8ct4c8supDl63/7fEI0nHTy/c3gCWeaaSPEbRI8ymM0kkH3a21OEBONVXTp86eSLp6K99yAOCv1qyetOm/gpqhZ5rjp88dfrMuT8rwXnekQQSiZStKt4MliXn0uVLF85v6UqeoByyZteh86taV16nX1MupV65km0By7JKTRKJlK1Vl2xwmIe3b9082p08o+vikxfTH072otbovC/t9t2HdeAU9CaRRCJlq14jXM7KHj+6PZY8RBn9x5OC+vNh1CqRqRlPXguPbquaJBIp20bdcsExFeUekJOn9Nhb2oDauT7UKgnZhQ3g6PqRRCJla6NaZgKHKYklj5FNfQ4g7TNqldG59eCYN6pJIpGytRPyHLyqSPKcjjuNgGGlP7VG/zzwXoWSPYlEylb1gwGcuq/Ig8bnA3gcJaNWGKEDx/iTmiQSKdsmgu+C07BPSZ6jSWEAy4Zgcp/i12pwMrpSUxKJlK0irh6coijyoOnZALLHKMhtEZlmsJh5SpJIpGwd+J62gGW6HkKeozleB2B/CLmr83kGnCv+5EgikbKVRb4Gx/hHiJw8ZuIzC6CLVpNb5CFJ9eCUj5bTe7IrOfkr+oCMWXssOTmSWiMxOXkUuUO5IGm2jDhHkpOHk6OOycnJIcQZeXB1MP2PGfszO/Cff+zZknqtQej2wgA1eYrX/ioAx3uRO9SfWa+1xu1qegvB3ZroSpwCYAV9OJLAiaPWqAYWkjsmAxhNHAaYQ45CAXxGrHY1wF7635IMztcffbbU6RZ45owpGhl5yNBHFkA/w5taJNdMyzCBl9GN3kYamnj9AWY7E57MNgHAcjeyjQBwj/6nzMJ/S7byCZUQVO0IbUeeodhYDuBSP2qJz6f7qiGonqL46LJd+uDBTGrZVWDToEGD/DyTbRSAfm5kKysC1tBHoPuDB3/Qe/EXsNHlwD948EDxUWRL6s0NEJif/BDioXDD75iA+u/bkysyn57LMk0QGA+r6W2zNTM2r/6T2e5y7zD5wLdk856zpV5zu5Eb2ZJm+lD6GPQFcui9KGxpDAGoPo5sqWOaBVam9G+7tpORByx/DeBOBDVP0T50WYYRor8H0Ftne5XsfXjZ6j2arY3LbFvBb8a8eR3+G7Kt/i/KVj68CI1Md37sr1HRe9ct1QAYVwZQM9QBkeufG9GoLFb9EWa7978y26n1ACKpzYT/x7JVfETZkiq+DjamrB3jgn0U9J4tygfwZKharXDSrKbntGSdBTbMr+3Jo9mqyYl2dutlJHK5i6zJ0keOh/F2L1uFwv1TTi1762y9W8hWriZH3jKii3Carasfy5RKcpMPiZodt0VOs3X/9XM/2+Fvk626jbIln0QD7FXfWDU0yEdJ71PwOQawHIiLHhPWQdXkvNeETNr51Ah7xvOdyXPZDtyUWmF6emyRkqy0Wm0U9fy9EjU3pxKrd3KRpS59nZIaef87+aW59EpCH7KaqdUmkCr+hcGceTKUeLtP5gJ4zH6xL4mjWXu/HOw+46mJg1qtAbjPbmYtYcr+dKY+ff9kEiVqtWOI+i757egbp1yvAzpU/7W5Owm82a8SToJorXY3cXqxCwPfyNZ7VYoO+gsj7bLdp9VOJM4PWu0ydoMco/HZsS7UyPfXGzqY8hL11+Lj44PInmbJIz2YlxeiyYmIXXcZc/7mYJrBDRJnsla7k6z2iuNDFLYnh4H+0dFQsurMPvQQirqsR9k16yOfpL1pBGrYFTuItUOrFQeqP7vMl1gyfix7n65EzY04YoUeL7LU3lurIJE4rMbGge/J/kN8Sgu12lVENFB7uR7A7+waOREt02rjSeDHLgojznatdgpRj/i9WrWwZvmpHEtxys8hbZEthaSZ0YSl8uqqkZ00ahm9N3GZFsDUYKzLPTreX0Y8RbuAPrG7Mxg0ZcmIkHsu2xVGCO6Jgw1gWpdy8I6rKNoAXnpnsur/HIK6eSTYAdynA+DVziJOHkTLifWVHlaJarJTAVEcsdqfgFWyr93FWHnEDNQ7ZjtcD17ZCGs9ACaQYBWQT5zBALo5Ztv1b/Asi23ZvgaWEucEkEJ/gFfRWGKcDlZnyMGIUlilhZCj6Qx4+aHbgQfEWWN3vSwClhHvJzMExr3yxnexCI8ygnfah98Iomxi5dhmv8cC6CD+aBsbUglekpKmGMG705Hs6SH6migSQA8SHAFSiWgSGimJKAU4QYJOAIYTJwv4mXYYAfgQa0g+BJWT2iJbWVQuHFmq722fOiDwvaXre6IOVrq1ITJSevt3HfrjqTwT3lAYrSZPZau8woV2+2yWBaiMasx25h0w+Uaw1vVrgCm/DqzTJIitByzZZ+/UADhoy3YaUKYDyxj6ZrZhVYD5yVUd33/HZrPtkQtWQSFY2SG2bI8Db2a7VAe81oNlmNK6bHvrwGLyTbDMdJ7tYkBXBlZNEPE+NwLlSQl7ud5/dKjWCKAhPcsA4K6cmloGTkkZ8HCHy2w3g1V+txisNbZsR+WDyTOCtaEV2U5P558da024QXz9Tnok2x2ANdvvjID5xen0egC/tEG2pIgrgxPmwtSEaeEd/bxV795uYHINRExCaI+R3yWm6+GM/od25LFs4wHzFhURDS8AHjdmm/N4nIzUCxmg5nrJXDXJhr4CLAOFOMqAjHAi8kkCMEbMtrQyIZgo8Ih4OeoXEaEDdkdERAQR0SngVV8i6rnKiOs+ZBMeEVEDbGQ30xDReYBZpSHSrGGA043nVxJQlH7xjEO2RaXxPkRdd1uAXEWrsk0GcHWAnJTRGUVOsy3Qr/In6vIHxDunlJnAFV9ifQuYIsneQyBtgjdR8AmIDTYKqAUaVgYRdfilrshVtiFGYM9AGVvQM8DYrzHbnL+GEam+0QN1nYgoOCJiP1DIDldfV9nmPOJev28ZoOqGbraKZMPzAcun1HTga4EEYeCdZKuJiFgGIJLdgFxkqzVBd//SBTVRVwa43YOIOlwAzP3bIFtSLa1ubNXSNN2SW4k/TAgL9vNVy+ntKVbo7Mu886LK4TAGWNVtbE/vlu3DUTYO2bYvAxYTr7cBmNmYrT9xlgAwCLH2tQDbiLMJyFQT7yzwUMwW84l3CbB42x1GkAfEEm/cCXWzU1IjActw4o0GMExcjWv2oYjZ6vsTby6Ama3JtrsROEo83wyn2eLLxiL1xBkIIJh414B1ZK97VroPceQZb3yPTADMw4g3weQqW4phNhMvrAGIb8z2pkpYC3GhbW8X2WZpiLMcQIMQaziATc1MSTnNljVFnJJykS3SR9huUb0jI95d4GJbZEteW+vAM+kKqwxwwBTcPrI6NrJnkL+vl1ouo1aT971nRrMsTMUL8R0ukxhI75itPYeefgEy5LaPazLFbDcQrx1ju+HvuvUqGswA40jQ0wJMtGZbI7f11s8xWy8Ag1r+ACgdSCKrE8BtcfUjNTnJ9qDdc8xoTba7gcoAEkx3mm2e3Vp+ywVAIQk2v3FOBvmTYAWgI3tqPZBIVjddZkuhZHUbSGzMdoYt0J3uZ7ueeL4G4JZtkH73QLa5viToa4ElnATDAHzaFtmS5jAjZPtk8dpruuoGJ3FVZt5IWr9wUmRo18AAjY+3WiWXkysyuVLt5dM+ILBz6KANOjTDzFRmn1k07Z5RqPZsV/JgttfEQllx4mUSwHQSZANHSLAP+JtYE4E6GVkVAmus2T4kQTCAaMdsSbxz0GW2ajMwiaxiAaNSWO1s12pxJ9Z3gEnhZraOZ3Afp9mKXYaLn/UkAM9sX/kJOTcFQDvHT4VHk9VOl9naHAOuN2Yrnv0XgBT3s40jQZ7tu8ZB4L4Hst1NVguAXLKSNwBz2yRbCjpt7fbhCM3wdVfyK+tNcMaoL3iYqt3508K4L4Z9GhrSMTgoIMBfo9G05/i2Z//NLyAgMDi4a6/+g8fFzFm6MflS+qtKI5yxNFSXZiTP79suylptw5+hsnfOtvaljUNPBbZC6VMA4eJMsu0CcZgEa6wdLBYL5fwFJDXOJFsZgHlvZHsGMO6NUrnOtr94oRaLCRNXu/7McZRQptvZ5gPryaq5mWRBAICJ1u8MVSTYD5wg5yYCaE92xgPoTFbb3cz2AHDTNpNMgt+Au+5nG2u7cB8kwS9Argey/ZmstgIpJMoENrVNttTlnNCtOTO2HXmHLzj6SKdnTGiWhdGXFGQ9TEs9d/Lo/j1b1vMSduzff/jE2Ss30l/kv66sc7W7oa4iN3VDdGcZeUc/FzY03OgjVuuRKSlvC5D/wiobQJxDtldt2a60drAfqH0hqgHuOWZb7yzb7pVgGV4mze7cfLZfA9CQVSCAaPey7QtgpPvZKkzA9+5l217MNhzAKOJ4Zb8ZGo34YcPR8ykp6Y7ZLgKgcDdbr9hl20+mpKTkO8t2z1tle8OW7RoPZ3se0L8QMcDZNsqWnUhkhKCKF7YnIlnQiGXJD4sr6xrMeO9qy3Ku7Z7VV02s9nPzLeA03I6Qkyez7QsH8S1n+yeaeuVWtjTkGQTGrd7NZbvCPtsAAEvcz3am+9l2BfBdK7OlVKA4htv+MlDoT00seA6RQ7YJAOTuZavZUgHRx5jtEzR1u62ypc7WblH5S6CMeAGD5+++9EJXWcMYLXh/6g/GiP+boMz/p3LwmJtitZ7KNgiAdr2dyJazPQ7krrez2L1sST79WA54qc1lO9cx25nuZ/ul+9n6vE22vqkAmOxyAOZRJBJHBob7F09otTcds13qdrbKGwAqbp3VarXZH2W2fwEZ6+3Mb7NsqfPZemtWSb1UJFJ1GTZv07n0vFIuXvPb1euw15NPyUrZI7HOWu2lPjLyXLZiMV+TI9fZ/gzcIXtuZCvSRC3PBTC1mWyHAAghq54APncv288B9LNm+0XL2VI5sKy12ZLyGASOvzb9EAuQFOj8ve0UcYFjtrmO2W4ETAtUxDnQqmzXvq9se7aU7UmX2R4ATpO9tsuWgrW14BlujmxHTfiFTfh+S/L1jDxdub6mjjGa3OnXYjY21Nfoy3V5OgNsGjYGk8B72PUG8OrOhMrI09neB9a0MttpQMVbZCvy0QMHmsk2AMAY+9kcjXvZzrDepqO2ALPdyPYhkNjqbAen6PccO7v7u2HkYB2gk5HzbCMADHoj26VAtWO2j8TH1KpsnwB73ke2AwBEucz2d+C6y2yXAk8/lGwpeF8VeOa8b/2cZKQO7DcyLn5j4umrD14W6MrKK/RVVbW1dUw90yBgGKa+trZar68oLyspznmclnJky5Jvxgz4+m8TRMa0gTLiyDTzss3gVR/qSuTxbI8BqST6zK1sw+2newNC3M1WTlaHgNRmsiUdsJJsp1khucz2V7LaD5QQp9h2Gu1xke1p4KmMBKPdzDYRRRpy6gKQ6nhxFflZ7G6F/F3MNgaAP/HUjJCtygD8QIJj7mebYnuos94l23Z2r/mfTrPdZDvqIKfZTgAagsmqa4e2zZZ8/51t7av+1AgNNUvt3yNy7MTJsbMW/bD8p4Qt2347xDu4a8uWX1cv/2HBjNhJE0YN+ax7ezkJYm6KN0bp/xyuIo7P8OR68MyvVmjoP5BtuBn4Uhx34wl1C9mKy66TwPueLtK9bL/KH2b7JGNfc9kuBSoDiRdcBfzbdbbP1MTrYwA2iU82Uy7ejNh8thMAzCfBYfey7Q/sJedOAX+TYLtjtnQOKPMjXlCFmG04Gu9N+xJCtrI624dSWe5nuwNgOhLv9LtkSzrgMvH8apxmuwDAEOKtd5qt7AmQTIKAzNywts2WvKY9YMAzPvm+m5Kc+0QV2DusW7Cft0JBn/zrH//8178++eQTIvZv//rXP//5j39+QkovTWDX0L6d231CPNmII09Lq+tqSp7vjVARS9555iMjeMzfs73ovWWbFmwTRJxXjT0dBcrjiDO2FkhoKVsx9WQNsQIuAmVezWeb23iYBWYw3xCnZ6WYjJNsVTnA3VBihaUDmUrX2ZovdiLWkFygXnheGwBsU3C5/W56M9taMVt6BNTwD8J7g9m9bMcBzIIhPbp3795FQU2tBUzCCT2p7o1sBwG4LzyjR2YxW3UJUDqYWJGlFiFbegBk8bvK9xpcZ7vSLtsYABc0RKRca3qnbE+L03TBt02N2UbbZdvHDDwOIdaMBqfZ0ngAu7yI1Yl78WRtmy2pRlyohEB/ZESA82q9eg4dMnAAe7eFf/t2ShnXK4uEbj8hmdLL1y+wa+/+EVHDBnhZu6WAib8kJiWuGeNDHM3gnUXiYa6MU9G7cf1LwF0DSjMzOxNRZz2Ae/tWnCoD8ErjTraUCEB36udtf1sghuA821T+MJm9iMJyARSfiv9+fy2Q6dNctjTZAjAXN2+5xACWr8h1trtMTMq2fWkmNN73p6kA8OLQxhNV9U5+SH4C6DIzvbjDgPXk2MaTOmxzL1tfI0Tm4ssjyE5fBmBSl8YsvoFV6UBWZgjZuQyg/tqO3anmiiNitvQdAOPlbbvTcFx8b7sEQOnxmd+syzNPMKI+81Kz2X4NGNhRXS3eDopi7YbDBdjyTtmGGgHc37/lHFN4ojHbPgDYIx0Rv8FDf2bjgWfY4jxbuggg//jqPc8BWMZTG2dLst57Ci3gmZ4tD/WmNynDYmK+GBUV0bdHR39vlVxGYprEp8tm2z6oW5/PokaMnzwlzC5JlZeSeF49Ft1hICg+2EdOHs12GiCezD3uQ/SwO7mVrWyNCVbMImouW7EO4Z1w4B2IqvpTs9nS+BJY6cZSC9kuTIDVXgUJZpsgWOxkSurf4HgTaw4Dwc2+7mVL02pg5zDZWQarjHYzwOpFdnxTILDM3t6YreIWBMUhYraym7DaSacAvGg2W+98cHYTJ1IPwfGx75Qt/QqBKdo2JUU3AYhvsrrkQ3C3RzPZqnZCVBVLbZityO/HpwwEzOVpIao3wu4yI37e9CkTR0eGBnk7TU7GvvcdOPKLydPmfDe3m/yN6jtFayshYJ4t8yfybLY07ykjnszKzfngZC5TkVvZsoalM2DVJw8gV9nSzAzGmi2pN74Gh9kVRC6ypeAzerD0p4OoxWzpx1KwChZSo6hcsEoWkZNsZUteGqzZUtQTMwDmgNrdmWSfFEsFrxacWWQnhj+o8YSGFBvzLdZsRbKEUrDyYsmWLSkSTGBdDyMxW/L6uQ6s8pXc/QKVLrKlnimljdlSyC2w6rcq3jFbiikHKyea7LLtcKSwMVvSnALLnKR5cyZZFP3YyEd7oAd9CNmSatz5UggspYfGdVJQE75fblm/etn3c2IGdlBQM+S+fSbM+m7J6vVbv9Y4rAkauf2VCYKyyxPV9B8WOH7BxH7UKsrP5kwd0p5aQz5o8sKYcB9qUc+42B7kHvXoudHB1ESPmDnDvKllvqMWTAwkd80vYWJI0G5KEXCd7Cn6RC/4shM1p1vczKFyErIVeUfOjenj8IgGfTNntBe1Vsfx88d0oHcn7zN1RqSSXPAfOf+LLuSSOnJ+7GBvog8jW5KFJGQxEKd5t43saJ/nJ903nzp2YNempUN9yQWvfvM37PjtyKkdoU2jjVr3pAECJmdzTxl9aCSfW3CcGk0Cyqn1bNlKPJ6tSB1zocQCgSlry4guahIpR1y/d+vqpeMzfVqa3NpzKTXtXtpYu12Do9b+zcCq9HKcN314JD8Ba+wiBvKkbD+ObEnWbd2TaliZsvd8GSIm5v1Nsa44P+9iOLWg48ZXBcUlr+f5ksCr65iNGQys6l6u7y6jD5BkBXBPbt/fCSnbjyBb8Wp5LI+xfQ5waGpYgIJYvt+ZzSaT4U4YtSBwB2M2m7FUQyy5X2jsgWwDrAyFpyep6IMkiQRwfqCaWL4jkgFMkbL9CLIV+c26XGyElaUiddmI7r5yUk+tB6sw3odcUo/7C6yGeT4k8wkZtvhSmRlWJt1f8Z1l9IGS7AdHX17OgLOEpGw/omxJ1nXJDZ0JIkN20qKobv7DXvLpPZ7agVzwGf47n/er0X4hn889/LIBIlPFvZ/7KenDJVn2EiLDH1+RlO1Hkq1IHrr6ts6IRsyL5PiZV4T6Mlf08aVmqEOmpTLg3Jy+6FBGHRqZyu5vHqSmD5skfO6yzYm71yyYFEBvp8vAgWHUJqRsWYq+P90oYGBjKBbvoqq5NH9AsJMClX6hMYmFEBTlN8CmQXd302AvEkkkUraeIe/1Q0pONZypTUuICe/m5yW3+119Ovcdt+z8awucqMtP/Slc7FwikbL1qOC4Q8/KDHDCXJF+ZOk3Yz8P78saMGhk3OI91wqdbmksf/n7gp4KEkkkUrYe5j0qITVbb4Izllpd1oO069dvpL8sqna+ianq1V9bx/uT50kkUrY2ipDY3TdzKhvQagZ97p1907sryfMkEilbB6qesVv/fF5cY4LbTPW6l1d3TQ9VUVuQSKRsWfKOoxYfvvmySN9gRgssptqSnNvJK8d3UVDbkEikbEWqbuN+3HMp/WVBWbXzeC2GOn1xdsb1Y2vjentR25JIpGxFcr9+4xclHDyf9vB5Zl7h65LSCr2+srREV5SX/SLj3pXk7ctiI7uo6UMhkUjZiuSazv2HR8/+ftnadZt37tq6fu3KH+Z+PW5Qj0DpGvv/7dOxAAAAAMAgf+tJ7CyHYGwLaAtoC9oC2gLagraAtoC2gLagLaAtoC1oC2gLaAvaAtoC2oK2gLaAtqAtoC2gLWgLaAtoC2gL2gLaAtqCtoC2gLagLaAtoC1oC2gLaAvaAtoC2gLagraAtoC2oC2gLaAtaAtoC2gL2gLaAtqCtoC2gLZAy1EI1wwY3vcAAAAASUVORK5CYII="
+    }
+   },
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<img src=\"../../../../../images/qiskit_header.png\" alt=\"Note: In order for images to show up in this jupyter notebook you need to select File => Trusted Notebook\" align=\"middle\">"
+    "![qiskit_header.png](attachment:qiskit_header.png)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# _*Qiskit Finance: Pricing Bull Spreads*_ \n",
-    "\n",
-    "The latest version of this notebook is available on https://github.com/Qiskit/qiskit-iqx-tutorials.\n",
-    "\n",
-    "***\n",
-    "### Contributors\n",
-    "Stefan Woerner<sup>[1]</sup>, Daniel Egger<sup>[1]</sup>\n",
-    "### Affliation\n",
-    "- <sup>[1]</sup>IBMQ"
+    "# Qiskit Finance: Pricing Bull Spreads"
    ]
   },
   {
@@ -55,14 +52,19 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:50:29.482202Z",
+     "start_time": "2019-12-12T17:50:25.441150Z"
+    }
+   },
    "outputs": [],
    "source": [
     "import matplotlib.pyplot as plt\n",
     "%matplotlib inline\n",
     "import numpy as np\n",
     "\n",
-    "from qiskit import BasicAer\n",
+    "from qiskit import Aer\n",
     "from qiskit.aqua.algorithms import AmplitudeEstimation\n",
     "from qiskit.aqua.components.uncertainty_models import LogNormalDistribution\n",
     "from qiskit.aqua.components.uncertainty_problems import UnivariateProblem\n",
@@ -86,7 +88,12 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:50:29.540739Z",
+     "start_time": "2019-12-12T17:50:29.525604Z"
+    }
+   },
    "outputs": [],
    "source": [
     "# number of qubits to represent the uncertainty\n",
@@ -116,7 +123,12 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:50:30.293077Z",
+     "start_time": "2019-12-12T17:50:29.586659Z"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -175,7 +187,12 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:50:30.356784Z",
+     "start_time": "2019-12-12T17:50:30.340964Z"
+    }
+   },
    "outputs": [],
    "source": [
     "# set the strike price (should be within the low and the high value of the uncertainty)\n",
@@ -213,7 +230,12 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:50:30.669532Z",
+     "start_time": "2019-12-12T17:50:30.390823Z"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -245,7 +267,12 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:50:30.752831Z",
+     "start_time": "2019-12-12T17:50:30.742849Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -274,7 +301,12 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:50:30.810295Z",
+     "start_time": "2019-12-12T17:50:30.802085Z"
+    }
+   },
    "outputs": [],
    "source": [
     "# set number of evaluation qubits (=log(samples))\n",
@@ -286,18 +318,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
+   "execution_count": 9,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:51:27.569545Z",
+     "start_time": "2019-12-12T17:50:33.522717Z"
+    }
+   },
    "outputs": [],
    "source": [
     "# result = ae.run(quantum_instance=BasicAer.get_backend('qasm_simulator'), shots=100)\n",
-    "result = ae.run(quantum_instance=BasicAer.get_backend('statevector_simulator'))"
+    "result = ae.run(quantum_instance=Aer.get_backend('statevector_simulator'))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
+   "execution_count": 10,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:51:27.620857Z",
+     "start_time": "2019-12-12T17:51:27.615249Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -317,8 +359,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
+   "execution_count": 11,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:51:28.158545Z",
+     "start_time": "2019-12-12T17:51:27.653954Z"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -381,8 +428,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
+   "execution_count": 12,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:51:28.210508Z",
+     "start_time": "2019-12-12T17:51:28.202207Z"
+    }
+   },
    "outputs": [],
    "source": [
     "# setup piecewise linear objective fcuntion\n",
@@ -413,8 +465,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
+   "execution_count": 13,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:51:28.255341Z",
+     "start_time": "2019-12-12T17:51:28.246180Z"
+    }
+   },
    "outputs": [],
    "source": [
     "# set number of evaluation qubits (=log(samples))\n",
@@ -426,18 +483,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
+   "execution_count": 15,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:52:26.499223Z",
+     "start_time": "2019-12-12T17:51:37.289794Z"
+    }
+   },
    "outputs": [],
    "source": [
     "# result_delta = ae_delta.run(quantum_instance=BasicAer.get_backend('qasm_simulator'), shots=100)\n",
-    "result_delta = ae_delta.run(quantum_instance=BasicAer.get_backend('statevector_simulator'))"
+    "result_delta = ae_delta.run(quantum_instance=Aer.get_backend('statevector_simulator'))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
+   "execution_count": 16,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:52:26.538733Z",
+     "start_time": "2019-12-12T17:52:26.532073Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -457,8 +524,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
+   "execution_count": 17,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:52:26.843582Z",
+     "start_time": "2019-12-12T17:52:26.569419Z"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -488,19 +560,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 18,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2019-08-22T01:55:52.763931Z",
-     "start_time": "2019-08-22T01:55:52.753702Z"
+     "end_time": "2019-12-12T17:52:27.016687Z",
+     "start_time": "2019-12-12T17:52:26.891140Z"
     }
    },
    "outputs": [
     {
      "data": {
       "text/html": [
-       "<h3>Version Information</h3><table><tr><th>Qiskit Software</th><th>Version</th></tr><tr><td>Qiskit</td><td>None</td></tr><tr><td>Terra</td><td>0.9.0</td></tr><tr><td>Aer</td><td>0.3.0</td></tr><tr><td>Ignis</td><td>0.2.0</td></tr><tr><td>Aqua</td><td>0.5.6</td></tr><tr><td>IBM Q Provider</td><td>0.3.2rc1</td></tr><tr><th>System information</th></tr><tr><td>Python</td><td>3.7.4 (default, Aug 13 2019, 15:17:50) \n",
-       "[Clang 4.0.1 (tags/RELEASE_401/final)]</td></tr><tr><td>OS</td><td>Darwin</td></tr><tr><td>CPUs</td><td>4</td></tr><tr><td>Memory (Gb)</td><td>16.0</td></tr><tr><td colspan='2'>Wed Aug 21 21:55:52 2019 EDT</td></tr></table>"
+       "<h3>Version Information</h3><table><tr><th>Qiskit Software</th><th>Version</th></tr><tr><td>Qiskit</td><td>0.14.0</td></tr><tr><td>Terra</td><td>0.11.0</td></tr><tr><td>Aer</td><td>0.3.4</td></tr><tr><td>Ignis</td><td>0.2.0</td></tr><tr><td>Aqua</td><td>0.6.1</td></tr><tr><td>IBM Q Provider</td><td>0.4.4</td></tr><tr><th>System information</th></tr><tr><td>Python</td><td>3.7.5 (default, Oct 25 2019, 10:52:18) \n",
+       "[Clang 4.0.1 (tags/RELEASE_401/final)]</td></tr><tr><td>OS</td><td>Darwin</td></tr><tr><td>CPUs</td><td>4</td></tr><tr><td>Memory (Gb)</td><td>16.0</td></tr><tr><td colspan='2'>Thu Dec 12 12:52:27 2019 EST</td></tr></table>"
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -552,7 +624,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.4"
+   "version": "3.7.5"
   },
   "varInspector": {
    "cols": {
diff --git a/qiskit/advanced/aqua/finance/simulation/credit_risk_analysis.ipynb b/qiskit/advanced/aqua/finance/simulation/credit_risk_analysis.ipynb
index 892fe45e9..8fc3e4d7d 100644
--- a/qiskit/advanced/aqua/finance/simulation/credit_risk_analysis.ipynb
+++ b/qiskit/advanced/aqua/finance/simulation/credit_risk_analysis.ipynb
@@ -1,26 +1,22 @@
 {
  "cells": [
   {
+   "attachments": {
+    "qiskit_header.png": {
+     "image/png": 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"
+    }
+   },
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<img src=\"../../../../../images/qiskit_header.png\" alt=\"Note: In order for images to show up in this jupyter notebook you need to select File => Trusted Notebook\" align=\"middle\">"
+    "![qiskit_header.png](attachment:qiskit_header.png)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# _*Qiskit Finance: Credit Risk Analysis*_ \n",
-    "\n",
-    "The latest version of this notebook is available on https://github.com/Qiskit/qiskit-iqx-tutorials.\n",
-    "\n",
-    "***\n",
-    "### Contributors\n",
-    "Stefan Woerner<sup>[1]</sup>, Daniel Egger<sup>[1]</sup>\n",
-    "\n",
-    "### Affliation\n",
-    "- <sup>[1]</sup>IBMQ"
+    "# Qiskit Finance: Credit Risk Analysis"
    ]
   },
   {
@@ -49,11 +45,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
+   "execution_count": 4,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:52:26.773593Z",
+     "start_time": "2019-12-12T17:52:26.766665Z"
+    }
+   },
    "outputs": [],
    "source": [
-    "from qiskit import QuantumRegister, QuantumCircuit, BasicAer, execute\n",
+    "from qiskit import QuantumRegister, QuantumCircuit, Aer, execute\n",
     "\n",
     "from qiskit.aqua.components.uncertainty_models import GaussianConditionalIndependenceModel as GCI\n",
     "from qiskit.aqua.components.uncertainty_problems import UnivariatePiecewiseLinearObjective as PwlObjective\n",
@@ -68,12 +69,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
+   "execution_count": 6,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:52:31.609401Z",
+     "start_time": "2019-12-12T17:52:31.602810Z"
+    }
+   },
    "outputs": [],
    "source": [
     "# define backend to be used\n",
-    "backend = BasicAer.get_backend('statevector_simulator')"
+    "backend = Aer.get_backend('statevector_simulator')"
    ]
   },
   {
@@ -117,8 +123,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
+   "execution_count": 7,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:52:32.252197Z",
+     "start_time": "2019-12-12T17:52:32.246744Z"
+    }
+   },
    "outputs": [],
    "source": [
     "# set problem parameters\n",
@@ -148,8 +159,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
+   "execution_count": 8,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:52:32.667519Z",
+     "start_time": "2019-12-12T17:52:32.658824Z"
+    }
+   },
    "outputs": [],
    "source": [
     "# construct circuit factory for uncertainty model (Gaussian Conditional Independence model)\n",
@@ -158,8 +174,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
+   "execution_count": 9,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:52:32.863444Z",
+     "start_time": "2019-12-12T17:52:32.836298Z"
+    }
+   },
    "outputs": [],
    "source": [
     "# determine the number of qubits required to represent the uncertainty model\n",
@@ -186,8 +207,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
+   "execution_count": 10,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:52:33.224440Z",
+     "start_time": "2019-12-12T17:52:33.169709Z"
+    }
+   },
    "outputs": [],
    "source": [
     "# run the circuit and analyze the results\n",
@@ -196,8 +222,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
+   "execution_count": 11,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:52:33.358445Z",
+     "start_time": "2019-12-12T17:52:33.339867Z"
+    }
+   },
    "outputs": [],
    "source": [
     "# analyze uncertainty circuit and determine exact solutions\n",
@@ -242,8 +273,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
+   "execution_count": 12,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:52:34.310896Z",
+     "start_time": "2019-12-12T17:52:33.500251Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -356,8 +392,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
+   "execution_count": 13,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:52:34.398210Z",
+     "start_time": "2019-12-12T17:52:34.390573Z"
+    }
+   },
    "outputs": [],
    "source": [
     "# determine number of qubits required to represent total loss\n",
@@ -369,8 +410,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
+   "execution_count": 14,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:52:34.495588Z",
+     "start_time": "2019-12-12T17:52:34.483985Z"
+    }
+   },
    "outputs": [],
    "source": [
     "# define linear objective function\n",
@@ -406,8 +452,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
+   "execution_count": 15,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:52:34.621639Z",
+     "start_time": "2019-12-12T17:52:34.592667Z"
+    }
+   },
    "outputs": [],
    "source": [
     "num_qubits = multivariate.num_target_qubits\n",
@@ -422,8 +473,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 16,
    "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:52:34.840139Z",
+     "start_time": "2019-12-12T17:52:34.779119Z"
+    },
     "scrolled": true
    },
    "outputs": [
@@ -464,97 +519,97 @@
        "«                                                                           »\n",
        "«q_a_2: ────────────────────────────────────────────────────────────────────»\n",
        "«                                                                           »\n",
-       "«                                                             »\n",
-       "«  q_0: ──■────■───────────────────────────────────────────■──»\n",
-       "«         │    │                                           │  »\n",
-       "«  q_1: ──┼────┼───────────────────────────────■───────────┼──»\n",
-       "«       ┌─┴─┐  │  ┌──────────────────┐       ┌─┴─┐         │  »\n",
-       "«  q_2: ┤ X ├──┼──┤ U3(-0.28365,0,0) ├───────┤ X ├─────────┼──»\n",
-       "«       └───┘┌─┴─┐└──────────────────┘┌──────┴───┴──────┐┌─┴─┐»\n",
-       "«  q_3: ─────┤ X ├────────────────────┤ U3(0.11174,0,0) ├┤ X ├»\n",
-       "«            └───┘                    └─────────────────┘└───┘»\n",
-       "«  q_4: ──────────────────────────────────────────────────────»\n",
-       "«                                                             »\n",
-       "«q_a_0: ──────────────────────────────────────────────────────»\n",
-       "«                                                             »\n",
-       "«q_a_1: ──────────────────────────────────────────────────────»\n",
-       "«                                                             »\n",
-       "«q_a_2: ──────────────────────────────────────────────────────»\n",
-       "«                                                             »\n",
+       "«                                                                           »\n",
+       "«  q_0: ──■────────────────────────■────────────────────────────────────────»\n",
+       "«         │                        │                                        »\n",
+       "«  q_1: ──┼────────────────────────┼───────────■────────────────────────────»\n",
+       "«       ┌─┴─┐┌──────────────────┐  │         ┌─┴─┐       ┌─────────────────┐»\n",
+       "«  q_2: ┤ X ├┤ U3(-0.28365,0,0) ├──┼─────────┤ X ├───────┤ U3(0.28365,0,0) ├»\n",
+       "«       └───┘└──────────────────┘┌─┴─┐┌──────┴───┴──────┐└─────────────────┘»\n",
+       "«  q_3: ─────────────────────────┤ X ├┤ U3(0.11174,0,0) ├───────────────────»\n",
+       "«                                └───┘└─────────────────┘                   »\n",
+       "«  q_4: ────────────────────────────────────────────────────────────────────»\n",
+       "«                                                                           »\n",
+       "«q_a_0: ────────────────────────────────────────────────────────────────────»\n",
+       "«                                                                           »\n",
+       "«q_a_1: ────────────────────────────────────────────────────────────────────»\n",
+       "«                                                                           »\n",
+       "«q_a_2: ────────────────────────────────────────────────────────────────────»\n",
+       "«                                                                           »\n",
+       "«                                                              ░           »\n",
+       "«  q_0: ──■────────────────────────────────────────────────────░───────────»\n",
+       "«         │                                                    ░           »\n",
+       "«  q_1: ──┼───────────■────────────■───────────────────────■───░───────────»\n",
+       "«         │         ┌─┴─┐          │                       │   ░           »\n",
+       "«  q_2: ──┼─────────┤ X ├──────────┼───────────────────────┼───░───■────■──»\n",
+       "«       ┌─┴─┐┌──────┴───┴───────┐┌─┴─┐┌─────────────────┐┌─┴─┐ ░   │    │  »\n",
+       "«  q_3: ┤ X ├┤ U3(-0.22349,0,0) ├┤ X ├┤ U3(0.22349,0,0) ├┤ X ├─░───┼────┼──»\n",
+       "«       └───┘└──────────────────┘└───┘└─────────────────┘└───┘ ░   │    │  »\n",
+       "«  q_4: ───────────────────────────────────────────────────────░───┼────┼──»\n",
+       "«                                                              ░   │  ┌─┴─┐»\n",
+       "«q_a_0: ───────────────────────────────────────────────────────░───■──┤ X ├»\n",
+       "«                                                              ░   │  └───┘»\n",
+       "«q_a_1: ───────────────────────────────────────────────────────░───┼───────»\n",
+       "«                                                              ░ ┌─┴─┐     »\n",
+       "«q_a_2: ───────────────────────────────────────────────────────░─┤ X ├─────»\n",
+       "«                                                              ░ └───┘     »\n",
+       "«                                      ░                                    »\n",
+       "«  q_0: ───────────────────────────────░────────────────────────────────────»\n",
+       "«                                      ░                                    »\n",
+       "«  q_1: ───────────────────────────────░────────────────────────────────────»\n",
+       "«                                      ░                                    »\n",
+       "«  q_2: ──■─────────■──────────────────░────────────────────────────────────»\n",
+       "«         │         │                  ░                                    »\n",
+       "«  q_3: ──┼─────────┼────■─────────■───░────────────────────────────────────»\n",
+       "«         │         │    │         │   ░ ┌───────────┐┌───────────────┐┌───┐»\n",
+       "«  q_4: ──┼─────────┼────┼─────────┼───░─┤ Ry(3pi/8) ├┤ U3(pi/24,0,0) ├┤ X ├»\n",
+       "«         │  ┌───┐  │    │  ┌───┐  │   ░ └───────────┘└───────────────┘└─┬─┘»\n",
+       "«q_a_0: ──┼──┤ X ├──■────┼──┤ X ├──┼───░─────────────────────────────────■──»\n",
+       "«       ┌─┴─┐└───┘  │  ┌─┴─┐└───┘┌─┴─┐ ░                                    »\n",
+       "«q_a_1: ┤ X ├───────┼──┤ X ├─────┤ X ├─░────────────────────────────────────»\n",
+       "«       └─┬─┘     ┌─┴─┐└───┘     └─┬─┘ ░                                    »\n",
+       "«q_a_2: ──■───────┤ X ├────────────■───░────────────────────────────────────»\n",
+       "«                 └───┘                ░                                    »\n",
        "«                                                                            ░ »\n",
        "«  q_0: ─────────────────────────────────────────────────────────────────────░─»\n",
        "«                                                                            ░ »\n",
-       "«  q_1: ────────────────────────────■────────────■───────────────────────■───░─»\n",
-       "«       ┌─────────────────┐       ┌─┴─┐          │                       │   ░ »\n",
-       "«  q_2: ┤ U3(0.28365,0,0) ├───────┤ X ├──────────┼───────────────────────┼───░─»\n",
-       "«       └─────────────────┘┌──────┴───┴───────┐┌─┴─┐┌─────────────────┐┌─┴─┐ ░ »\n",
-       "«  q_3: ───────────────────┤ U3(-0.22349,0,0) ├┤ X ├┤ U3(0.22349,0,0) ├┤ X ├─░─»\n",
-       "«                          └──────────────────┘└───┘└─────────────────┘└───┘ ░ »\n",
-       "«  q_4: ─────────────────────────────────────────────────────────────────────░─»\n",
+       "«  q_1: ─────────────────────────────────────────────────────────────────────░─»\n",
        "«                                                                            ░ »\n",
-       "«q_a_0: ─────────────────────────────────────────────────────────────────────░─»\n",
+       "«  q_2: ─────────────────────────────────────────────────────────────────────░─»\n",
        "«                                                                            ░ »\n",
-       "«q_a_1: ─────────────────────────────────────────────────────────────────────░─»\n",
+       "«  q_3: ─────────────────────────────────────────────────────────────────────░─»\n",
+       "«       ┌────────────────┐┌───┐┌───────────────┐┌───┐┌────────────────┐┌───┐ ░ »\n",
+       "«  q_4: ┤ U3(-pi/24,0,0) ├┤ X ├┤ U3(pi/12,0,0) ├┤ X ├┤ U3(-pi/12,0,0) ├┤ X ├─░─»\n",
+       "«       └────────────────┘└─┬─┘└───────────────┘└─┬─┘└────────────────┘└─┬─┘ ░ »\n",
+       "«q_a_0: ────────────────────■─────────────────────┼──────────────────────┼───░─»\n",
+       "«                                                 │                      │   ░ »\n",
+       "«q_a_1: ──────────────────────────────────────────■──────────────────────■───░─»\n",
        "«                                                                            ░ »\n",
        "«q_a_2: ─────────────────────────────────────────────────────────────────────░─»\n",
        "«                                                                            ░ »\n",
-       "«                                                ░              »\n",
-       "«  q_0: ─────────────────────────────────────────░──────────────»\n",
-       "«                                                ░              »\n",
-       "«  q_1: ─────────────────────────────────────────░──────────────»\n",
-       "«                                                ░              »\n",
-       "«  q_2: ──■────■────■─────────■──────────────────░──────────────»\n",
-       "«         │    │    │         │                  ░              »\n",
-       "«  q_3: ──┼────┼────┼─────────┼────■────■────────░──────────────»\n",
-       "«         │    │    │         │    │    │        ░ ┌───────────┐»\n",
-       "«  q_4: ──┼────┼────┼─────────┼────┼────┼────────░─┤ Ry(3pi/8) ├»\n",
-       "«         │  ┌─┴─┐  │  ┌───┐  │    │    │  ┌───┐ ░ └───────────┘»\n",
-       "«q_a_0: ──■──┤ X ├──┼──┤ X ├──■────┼────┼──┤ X ├─░──────────────»\n",
-       "«         │  └───┘┌─┴─┐└───┘  │  ┌─┴─┐┌─┴─┐└───┘ ░              »\n",
-       "«q_a_1: ──┼───────┤ X ├───────┼──┤ X ├┤ X ├──────░──────────────»\n",
-       "«       ┌─┴─┐     └─┬─┘     ┌─┴─┐└───┘└─┬─┘      ░              »\n",
-       "«q_a_2: ┤ X ├───────■───────┤ X ├───────■────────░──────────────»\n",
-       "«       └───┘               └───┘                ░              »\n",
-       "«                                                                          »\n",
-       "«  q_0: ───────────────────────────────────────────────────────────────────»\n",
-       "«                                                                          »\n",
-       "«  q_1: ───────────────────────────────────────────────────────────────────»\n",
-       "«                                                                          »\n",
-       "«  q_2: ───────────────────────────────────────────────────────────────────»\n",
-       "«                                                                          »\n",
-       "«  q_3: ───────────────────────────────────────────────────────────────────»\n",
-       "«       ┌───────────────┐┌───┐┌────────────────┐┌───┐┌───────────────┐┌───┐»\n",
-       "«  q_4: ┤ U3(pi/24,0,0) ├┤ X ├┤ U3(-pi/24,0,0) ├┤ X ├┤ U3(pi/12,0,0) ├┤ X ├»\n",
-       "«       └───────────────┘└─┬─┘└────────────────┘└─┬─┘└───────────────┘└─┬─┘»\n",
-       "«q_a_0: ───────────────────■──────────────────────■─────────────────────┼──»\n",
-       "«                                                                       │  »\n",
-       "«q_a_1: ────────────────────────────────────────────────────────────────■──»\n",
-       "«                                                                          »\n",
-       "«q_a_2: ───────────────────────────────────────────────────────────────────»\n",
-       "«                                                                          »\n",
-       "«                               ░                                          ░ \n",
-       "«  q_0: ────────────────────────░──────────────────────────────────────────░─\n",
-       "«                               ░                                          ░ \n",
-       "«  q_1: ────────────────────────░──────────────────────────────────────────░─\n",
-       "«                               ░                                          ░ \n",
-       "«  q_2: ────────────────────────░──────────────────■────■─────────■────■───░─\n",
-       "«                               ░                  │    │         │    │   ░ \n",
-       "«  q_3: ────────────────────────░───■─────────■────┼────┼─────────┼────┼───░─\n",
-       "«       ┌────────────────┐┌───┐ ░   │         │    │    │         │    │   ░ \n",
-       "«  q_4: ┤ U3(-pi/12,0,0) ├┤ X ├─░───┼─────────┼────┼────┼─────────┼────┼───░─\n",
-       "«       └────────────────┘└─┬─┘ ░   │  ┌───┐  │    │    │  ┌───┐┌─┴─┐  │   ░ \n",
-       "«q_a_0: ────────────────────┼───░───┼──┤ X ├──┼────■────┼──┤ X ├┤ X ├──■───░─\n",
-       "«                           │   ░ ┌─┴─┐└───┘┌─┴─┐  │  ┌─┴─┐└───┘└───┘  │   ░ \n",
-       "«q_a_1: ────────────────────■───░─┤ X ├─────┤ X ├──┼──┤ X ├────────────┼───░─\n",
-       "«                               ░ └─┬─┘     └───┘┌─┴─┐└─┬─┘          ┌─┴─┐ ░ \n",
-       "«q_a_2: ────────────────────────░───■────────────┤ X ├──■────────────┤ X ├─░─\n",
-       "«                               ░                └───┘               └───┘ ░ </pre>"
+       "«                                                ░ \n",
+       "«  q_0: ─────────────────────────────────────────░─\n",
+       "«                                                ░ \n",
+       "«  q_1: ─────────────────────────────────────────░─\n",
+       "«                                                ░ \n",
+       "«  q_2: ─────────────────■─────────■────■────■───░─\n",
+       "«                        │         │    │    │   ░ \n",
+       "«  q_3: ──■─────────■────┼─────────┼────┼────┼───░─\n",
+       "«         │         │    │         │    │    │   ░ \n",
+       "«  q_4: ──┼─────────┼────┼─────────┼────┼────┼───░─\n",
+       "«         │  ┌───┐  │    │  ┌───┐  │  ┌─┴─┐  │   ░ \n",
+       "«q_a_0: ──┼──┤ X ├──┼────■──┤ X ├──┼──┤ X ├──■───░─\n",
+       "«       ┌─┴─┐└───┘┌─┴─┐  │  └───┘┌─┴─┐└───┘  │   ░ \n",
+       "«q_a_1: ┤ X ├─────┤ X ├──┼───────┤ X ├───────┼───░─\n",
+       "«       └─┬─┘     └───┘┌─┴─┐     └─┬─┘     ┌─┴─┐ ░ \n",
+       "«q_a_2: ──■────────────┤ X ├───────■───────┤ X ├─░─\n",
+       "«                      └───┘               └───┘ ░ </pre>"
       ],
       "text/plain": [
-       "<qiskit.visualization.text.TextDrawing at 0x1d89ace47f0>"
+       "<qiskit.visualization.text.TextDrawing at 0x10fd82210>"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -565,8 +620,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
+   "execution_count": 17,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:52:35.675198Z",
+     "start_time": "2019-12-12T17:52:35.491437Z"
+    }
+   },
    "outputs": [],
    "source": [
     "job = execute(qc, backend=BasicAer.get_backend('statevector_simulator'))"
@@ -574,8 +634,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
+   "execution_count": 18,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:52:35.831081Z",
+     "start_time": "2019-12-12T17:52:35.817604Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -610,8 +675,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 19,
    "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:53:12.143311Z",
+     "start_time": "2019-12-12T17:52:36.882344Z"
+    },
     "scrolled": false
    },
    "outputs": [
@@ -640,8 +709,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
+   "execution_count": 20,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:53:12.539348Z",
+     "start_time": "2019-12-12T17:53:12.232278Z"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -657,7 +731,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -720,8 +794,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
+   "execution_count": 21,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:53:12.630621Z",
+     "start_time": "2019-12-12T17:53:12.625898Z"
+    }
+   },
    "outputs": [],
    "source": [
     "# define value x to evaluate the CDF(x)\n",
@@ -745,8 +824,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
+   "execution_count": 22,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:53:12.722599Z",
+     "start_time": "2019-12-12T17:53:12.719444Z"
+    }
+   },
    "outputs": [],
    "source": [
     "# set x value to estimate the CDF\n",
@@ -755,8 +839,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
+   "execution_count": 23,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:53:12.828110Z",
+     "start_time": "2019-12-12T17:53:12.806429Z"
+    }
+   },
    "outputs": [],
    "source": [
     "# get operator\n",
@@ -776,17 +865,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
+   "execution_count": 25,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:53:13.196976Z",
+     "start_time": "2019-12-12T17:53:13.140090Z"
+    }
+   },
    "outputs": [],
    "source": [
-    "job = execute(qc, backend=BasicAer.get_backend('statevector_simulator'))"
+    "job = execute(qc, backend=Aer.get_backend('statevector_simulator'))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 26,
    "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:53:13.308938Z",
+     "start_time": "2019-12-12T17:53:13.276423Z"
+    },
     "scrolled": true
    },
    "outputs": [
@@ -827,80 +925,80 @@
        "«                                                                           »\n",
        "«q_a_2: ────────────────────────────────────────────────────────────────────»\n",
        "«                                                                           »\n",
-       "«                                                             »\n",
-       "«  q_0: ──■────■───────────────────────────────────────────■──»\n",
-       "«         │    │                                           │  »\n",
-       "«  q_1: ──┼────┼───────────────────────────────■───────────┼──»\n",
-       "«       ┌─┴─┐  │  ┌──────────────────┐       ┌─┴─┐         │  »\n",
-       "«  q_2: ┤ X ├──┼──┤ U3(-0.28365,0,0) ├───────┤ X ├─────────┼──»\n",
-       "«       └───┘┌─┴─┐└──────────────────┘┌──────┴───┴──────┐┌─┴─┐»\n",
-       "«  q_3: ─────┤ X ├────────────────────┤ U3(0.11174,0,0) ├┤ X ├»\n",
-       "«            └───┘                    └─────────────────┘└───┘»\n",
-       "«  q_4: ──────────────────────────────────────────────────────»\n",
-       "«                                                             »\n",
-       "«q_a_0: ──────────────────────────────────────────────────────»\n",
-       "«                                                             »\n",
-       "«q_a_1: ──────────────────────────────────────────────────────»\n",
-       "«                                                             »\n",
-       "«q_a_2: ──────────────────────────────────────────────────────»\n",
-       "«                                                             »\n",
-       "«                                                                            ░ »\n",
-       "«  q_0: ─────────────────────────────────────────────────────────────────────░─»\n",
-       "«                                                                            ░ »\n",
-       "«  q_1: ────────────────────────────■────────────■───────────────────────■───░─»\n",
-       "«       ┌─────────────────┐       ┌─┴─┐          │                       │   ░ »\n",
-       "«  q_2: ┤ U3(0.28365,0,0) ├───────┤ X ├──────────┼───────────────────────┼───░─»\n",
-       "«       └─────────────────┘┌──────┴───┴───────┐┌─┴─┐┌─────────────────┐┌─┴─┐ ░ »\n",
-       "«  q_3: ───────────────────┤ U3(-0.22349,0,0) ├┤ X ├┤ U3(0.22349,0,0) ├┤ X ├─░─»\n",
-       "«                          └──────────────────┘└───┘└─────────────────┘└───┘ ░ »\n",
-       "«  q_4: ─────────────────────────────────────────────────────────────────────░─»\n",
-       "«                                                                            ░ »\n",
-       "«q_a_0: ─────────────────────────────────────────────────────────────────────░─»\n",
-       "«                                                                            ░ »\n",
-       "«q_a_1: ─────────────────────────────────────────────────────────────────────░─»\n",
-       "«                                                                            ░ »\n",
-       "«q_a_2: ─────────────────────────────────────────────────────────────────────░─»\n",
-       "«                                                                            ░ »\n",
-       "«                                                ░                 ░           »\n",
-       "«  q_0: ─────────────────────────────────────────░─────────────────░───────────»\n",
-       "«                                                ░                 ░           »\n",
-       "«  q_1: ─────────────────────────────────────────░─────────────────░───────────»\n",
-       "«                                                ░                 ░           »\n",
-       "«  q_2: ──■────■────■─────────■──────────────────░─────────────────░───────────»\n",
-       "«         │    │    │         │                  ░                 ░           »\n",
-       "«  q_3: ──┼────┼────┼─────────┼────■────■────────░─────────────────░───■───────»\n",
-       "«         │    │    │         │    │    │        ░      ┌───┐┌───┐ ░   │       »\n",
-       "«  q_4: ──┼────┼────┼─────────┼────┼────┼────────░──────┤ X ├┤ X ├─░───┼───────»\n",
-       "«         │  ┌─┴─┐  │  ┌───┐  │    │    │  ┌───┐ ░      └─┬─┘└───┘ ░   │  ┌───┐»\n",
-       "«q_a_0: ──■──┤ X ├──┼──┤ X ├──■────┼────┼──┤ X ├─░───■────┼────■───░───┼──┤ X ├»\n",
-       "«         │  └───┘┌─┴─┐└───┘  │  ┌─┴─┐┌─┴─┐└───┘ ░   │    │    │   ░ ┌─┴─┐└───┘»\n",
-       "«q_a_1: ──┼───────┤ X ├───────┼──┤ X ├┤ X ├──────░───┼────■────┼───░─┤ X ├─────»\n",
-       "«       ┌─┴─┐     └─┬─┘     ┌─┴─┐└───┘└─┬─┘      ░ ┌─┴─┐  │  ┌─┴─┐ ░ └─┬─┘     »\n",
-       "«q_a_2: ┤ X ├───────■───────┤ X ├───────■────────░─┤ X ├──■──┤ X ├─░───■───────»\n",
-       "«       └───┘               └───┘                ░ └───┘     └───┘ ░           »\n",
-       "«                                      ░ \n",
-       "«  q_0: ───────────────────────────────░─\n",
-       "«                                      ░ \n",
-       "«  q_1: ───────────────────────────────░─\n",
-       "«                                      ░ \n",
-       "«  q_2: ───────■────■─────────■────■───░─\n",
-       "«              │    │         │    │   ░ \n",
-       "«  q_3: ──■────┼────┼─────────┼────┼───░─\n",
-       "«         │    │    │         │    │   ░ \n",
-       "«  q_4: ──┼────┼────┼─────────┼────┼───░─\n",
-       "«         │    │    │  ┌───┐┌─┴─┐  │   ░ \n",
-       "«q_a_0: ──┼────■────┼──┤ X ├┤ X ├──■───░─\n",
-       "«       ┌─┴─┐  │  ┌─┴─┐└───┘└───┘  │   ░ \n",
-       "«q_a_1: ┤ X ├──┼──┤ X ├────────────┼───░─\n",
-       "«       └───┘┌─┴─┐└─┬─┘          ┌─┴─┐ ░ \n",
-       "«q_a_2: ─────┤ X ├──■────────────┤ X ├─░─\n",
-       "«            └───┘               └───┘ ░ </pre>"
+       "«                                                                           »\n",
+       "«  q_0: ──■────────────────────────■────────────────────────────────────────»\n",
+       "«         │                        │                                        »\n",
+       "«  q_1: ──┼────────────────────────┼───────────■────────────────────────────»\n",
+       "«       ┌─┴─┐┌──────────────────┐  │         ┌─┴─┐       ┌─────────────────┐»\n",
+       "«  q_2: ┤ X ├┤ U3(-0.28365,0,0) ├──┼─────────┤ X ├───────┤ U3(0.28365,0,0) ├»\n",
+       "«       └───┘└──────────────────┘┌─┴─┐┌──────┴───┴──────┐└─────────────────┘»\n",
+       "«  q_3: ─────────────────────────┤ X ├┤ U3(0.11174,0,0) ├───────────────────»\n",
+       "«                                └───┘└─────────────────┘                   »\n",
+       "«  q_4: ────────────────────────────────────────────────────────────────────»\n",
+       "«                                                                           »\n",
+       "«q_a_0: ────────────────────────────────────────────────────────────────────»\n",
+       "«                                                                           »\n",
+       "«q_a_1: ────────────────────────────────────────────────────────────────────»\n",
+       "«                                                                           »\n",
+       "«q_a_2: ────────────────────────────────────────────────────────────────────»\n",
+       "«                                                                           »\n",
+       "«                                                              ░           »\n",
+       "«  q_0: ──■────────────────────────────────────────────────────░───────────»\n",
+       "«         │                                                    ░           »\n",
+       "«  q_1: ──┼───────────■────────────■───────────────────────■───░───────────»\n",
+       "«         │         ┌─┴─┐          │                       │   ░           »\n",
+       "«  q_2: ──┼─────────┤ X ├──────────┼───────────────────────┼───░───■────■──»\n",
+       "«       ┌─┴─┐┌──────┴───┴───────┐┌─┴─┐┌─────────────────┐┌─┴─┐ ░   │    │  »\n",
+       "«  q_3: ┤ X ├┤ U3(-0.22349,0,0) ├┤ X ├┤ U3(0.22349,0,0) ├┤ X ├─░───┼────┼──»\n",
+       "«       └───┘└──────────────────┘└───┘└─────────────────┘└───┘ ░   │    │  »\n",
+       "«  q_4: ───────────────────────────────────────────────────────░───┼────┼──»\n",
+       "«                                                              ░   │  ┌─┴─┐»\n",
+       "«q_a_0: ───────────────────────────────────────────────────────░───■──┤ X ├»\n",
+       "«                                                              ░   │  └───┘»\n",
+       "«q_a_1: ───────────────────────────────────────────────────────░───┼───────»\n",
+       "«                                                              ░ ┌─┴─┐     »\n",
+       "«q_a_2: ───────────────────────────────────────────────────────░─┤ X ├─────»\n",
+       "«                                                              ░ └───┘     »\n",
+       "«                                      ░                 ░                     »\n",
+       "«  q_0: ───────────────────────────────░─────────────────░─────────────────────»\n",
+       "«                                      ░                 ░                     »\n",
+       "«  q_1: ───────────────────────────────░─────────────────░─────────────────────»\n",
+       "«                                      ░                 ░                     »\n",
+       "«  q_2: ──■─────────■──────────────────░─────────────────░──────────────────■──»\n",
+       "«         │         │                  ░                 ░                  │  »\n",
+       "«  q_3: ──┼─────────┼────■─────────■───░─────────────────░───■─────────■────┼──»\n",
+       "«         │         │    │         │   ░      ┌───┐┌───┐ ░   │         │    │  »\n",
+       "«  q_4: ──┼─────────┼────┼─────────┼───░──────┤ X ├┤ X ├─░───┼─────────┼────┼──»\n",
+       "«         │  ┌───┐  │    │  ┌───┐  │   ░      └─┬─┘└───┘ ░   │  ┌───┐  │    │  »\n",
+       "«q_a_0: ──┼──┤ X ├──■────┼──┤ X ├──┼───░───■────┼────■───░───┼──┤ X ├──┼────■──»\n",
+       "«       ┌─┴─┐└───┘  │  ┌─┴─┐└───┘┌─┴─┐ ░   │    │    │   ░ ┌─┴─┐└───┘┌─┴─┐  │  »\n",
+       "«q_a_1: ┤ X ├───────┼──┤ X ├─────┤ X ├─░───┼────■────┼───░─┤ X ├─────┤ X ├──┼──»\n",
+       "«       └─┬─┘     ┌─┴─┐└───┘     └─┬─┘ ░ ┌─┴─┐  │  ┌─┴─┐ ░ └─┬─┘     └───┘┌─┴─┐»\n",
+       "«q_a_2: ──■───────┤ X ├────────────■───░─┤ X ├──■──┤ X ├─░───■────────────┤ X ├»\n",
+       "«                 └───┘                ░ └───┘     └───┘ ░                └───┘»\n",
+       "«                            ░ \n",
+       "«  q_0: ─────────────────────░─\n",
+       "«                            ░ \n",
+       "«  q_1: ─────────────────────░─\n",
+       "«                            ░ \n",
+       "«  q_2: ───────■────■────■───░─\n",
+       "«              │    │    │   ░ \n",
+       "«  q_3: ───────┼────┼────┼───░─\n",
+       "«              │    │    │   ░ \n",
+       "«  q_4: ───────┼────┼────┼───░─\n",
+       "«       ┌───┐  │  ┌─┴─┐  │   ░ \n",
+       "«q_a_0: ┤ X ├──┼──┤ X ├──■───░─\n",
+       "«       └───┘┌─┴─┐└───┘  │   ░ \n",
+       "«q_a_1: ─────┤ X ├───────┼───░─\n",
+       "«            └─┬─┘     ┌─┴─┐ ░ \n",
+       "«q_a_2: ───────■───────┤ X ├─░─\n",
+       "«                      └───┘ ░ </pre>"
       ],
       "text/plain": [
-       "<qiskit.visualization.text.TextDrawing at 0x1d8a5b32940>"
+       "<qiskit.visualization.text.TextDrawing at 0x1a2f188c10>"
       ]
      },
-     "execution_count": 21,
+     "execution_count": 26,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -911,8 +1009,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
+   "execution_count": 27,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:53:13.404462Z",
+     "start_time": "2019-12-12T17:53:13.395230Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -944,8 +1047,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
+   "execution_count": 28,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:53:25.281790Z",
+     "start_time": "2019-12-12T17:53:13.489005Z"
+    }
+   },
    "outputs": [],
    "source": [
     "# run amplitude estimation\n",
@@ -957,8 +1065,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
+   "execution_count": 29,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:53:25.534493Z",
+     "start_time": "2019-12-12T17:53:25.377374Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -1011,8 +1124,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
+   "execution_count": 30,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:53:25.664138Z",
+     "start_time": "2019-12-12T17:53:25.655593Z"
+    }
+   },
    "outputs": [],
    "source": [
     "def run_ae_for_cdf(x_eval, num_eval_qubits=3, simulator='statevector_simulator'):\n",
@@ -1027,8 +1145,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
+   "execution_count": 31,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:53:25.777242Z",
+     "start_time": "2019-12-12T17:53:25.764174Z"
+    }
+   },
    "outputs": [],
    "source": [
     "def bisection_search(objective, target_value, low_level, high_level, low_value=None, high_value=None):\n",
@@ -1095,8 +1218,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
+   "execution_count": 32,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:53:54.092265Z",
+     "start_time": "2019-12-12T17:53:25.882181Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -1125,8 +1253,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
+   "execution_count": 33,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:53:54.206924Z",
+     "start_time": "2019-12-12T17:53:54.201136Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -1165,8 +1298,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
+   "execution_count": 34,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:53:54.324451Z",
+     "start_time": "2019-12-12T17:53:54.318093Z"
+    }
+   },
    "outputs": [],
    "source": [
     "# define linear objective\n",
@@ -1192,8 +1330,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
+   "execution_count": 35,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:53:54.430712Z",
+     "start_time": "2019-12-12T17:53:54.425804Z"
+    }
+   },
    "outputs": [],
    "source": [
     "var = 2"
@@ -1201,8 +1344,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
+   "execution_count": 36,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:53:54.533399Z",
+     "start_time": "2019-12-12T17:53:54.529484Z"
+    }
+   },
    "outputs": [],
    "source": [
     "multivariate_cvar = MultivariateProblem(u, agg, cvar_objective)"
@@ -1210,8 +1358,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
+   "execution_count": 37,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:53:54.647686Z",
+     "start_time": "2019-12-12T17:53:54.619286Z"
+    }
+   },
    "outputs": [],
    "source": [
     "num_qubits = multivariate_cvar.num_target_qubits\n",
@@ -1233,8 +1386,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
+   "execution_count": 38,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:53:55.069620Z",
+     "start_time": "2019-12-12T17:53:54.764564Z"
+    }
+   },
    "outputs": [],
    "source": [
     "job = execute(qc, backend=BasicAer.get_backend('statevector_simulator'))"
@@ -1242,8 +1400,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
+   "execution_count": 39,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:53:55.239666Z",
+     "start_time": "2019-12-12T17:53:55.217521Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -1279,8 +1442,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
+   "execution_count": 40,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:59:28.975921Z",
+     "start_time": "2019-12-12T17:53:55.370591Z"
+    }
+   },
    "outputs": [],
    "source": [
     "# run amplitude estimation\n",
@@ -1292,8 +1460,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {},
+   "execution_count": 41,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:59:29.082789Z",
+     "start_time": "2019-12-12T17:59:29.077755Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -1314,8 +1487,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {},
+   "execution_count": 42,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:59:29.601663Z",
+     "start_time": "2019-12-12T17:59:29.208078Z"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -1368,19 +1546,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 43,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2019-08-22T01:56:12.651056Z",
-     "start_time": "2019-08-22T01:56:12.640412Z"
+     "end_time": "2019-12-12T17:59:29.776957Z",
+     "start_time": "2019-12-12T17:59:29.705383Z"
     }
    },
    "outputs": [
     {
      "data": {
       "text/html": [
-       "<h3>Version Information</h3><table><tr><th>Qiskit Software</th><th>Version</th></tr><tr><td>Qiskit</td><td>None</td></tr><tr><td>Terra</td><td>0.9.0</td></tr><tr><td>Aer</td><td>0.3.0</td></tr><tr><td>Ignis</td><td>0.2.0</td></tr><tr><td>Aqua</td><td>0.5.6</td></tr><tr><td>IBM Q Provider</td><td>0.3.2rc1</td></tr><tr><th>System information</th></tr><tr><td>Python</td><td>3.7.4 (default, Aug 13 2019, 15:17:50) \n",
-       "[Clang 4.0.1 (tags/RELEASE_401/final)]</td></tr><tr><td>OS</td><td>Darwin</td></tr><tr><td>CPUs</td><td>4</td></tr><tr><td>Memory (Gb)</td><td>16.0</td></tr><tr><td colspan='2'>Wed Aug 21 21:56:12 2019 EDT</td></tr></table>"
+       "<h3>Version Information</h3><table><tr><th>Qiskit Software</th><th>Version</th></tr><tr><td>Qiskit</td><td>0.14.0</td></tr><tr><td>Terra</td><td>0.11.0</td></tr><tr><td>Aer</td><td>0.3.4</td></tr><tr><td>Ignis</td><td>0.2.0</td></tr><tr><td>Aqua</td><td>0.6.1</td></tr><tr><td>IBM Q Provider</td><td>0.4.4</td></tr><tr><th>System information</th></tr><tr><td>Python</td><td>3.7.5 (default, Oct 25 2019, 10:52:18) \n",
+       "[Clang 4.0.1 (tags/RELEASE_401/final)]</td></tr><tr><td>OS</td><td>Darwin</td></tr><tr><td>CPUs</td><td>4</td></tr><tr><td>Memory (Gb)</td><td>16.0</td></tr><tr><td colspan='2'>Thu Dec 12 12:59:29 2019 EST</td></tr></table>"
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -1432,7 +1610,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.4"
+   "version": "3.7.5"
   },
   "varInspector": {
    "cols": {
diff --git a/qiskit/advanced/aqua/finance/simulation/european_call_option_pricing.ipynb b/qiskit/advanced/aqua/finance/simulation/european_call_option_pricing.ipynb
index 1de301fda..c9292425f 100644
--- a/qiskit/advanced/aqua/finance/simulation/european_call_option_pricing.ipynb
+++ b/qiskit/advanced/aqua/finance/simulation/european_call_option_pricing.ipynb
@@ -1,25 +1,22 @@
 {
  "cells": [
   {
+   "attachments": {
+    "qiskit_header.png": {
+     "image/png": 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"
+    }
+   },
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<img src=\"../../../../../images/qiskit_header.png\" alt=\"Note: In order for images to show up in this jupyter notebook you need to select File => Trusted Notebook\" align=\"middle\">"
+    "![qiskit_header.png](attachment:qiskit_header.png)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# _*Qiskit Finance: Pricing European Call Options*_ \n",
-    "\n",
-    "The latest version of this notebook is available on https://github.com/Qiskit/qiskit-iqx-tutorials.\n",
-    "\n",
-    "***\n",
-    "### Contributors\n",
-    "Stefan Woerner<sup>[1]</sup>, Daniel Egger<sup>[1]</sup>, Shaohan Hu<sup>[1]</sup>, Stephen Wood<sup>[1]</sup>, Marco Pistoia<sup>[1]</sup>\n",
-    "### Affliation\n",
-    "- <sup>[1]</sup>IBMQ"
+    "# Qiskit Finance: Pricing European Call Options"
    ]
   },
   {
@@ -55,7 +52,12 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:53:55.501891Z",
+     "start_time": "2019-12-12T17:53:52.400301Z"
+    }
+   },
    "outputs": [],
    "source": [
     "import matplotlib.pyplot as plt\n",
@@ -487,14 +489,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
+   "execution_count": 2,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:53:59.261238Z",
+     "start_time": "2019-12-12T17:53:59.136330Z"
+    }
+   },
    "outputs": [
     {
      "data": {
       "text/html": [
-       "<h3>Version Information</h3><table><tr><th>Qiskit Software</th><th>Version</th></tr><tr><td>Qiskit</td><td>0.12.0</td></tr><tr><td>Terra</td><td>0.9.0</td></tr><tr><td>Aer</td><td>0.3.0</td></tr><tr><td>Ignis</td><td>0.2.0</td></tr><tr><td>Aqua</td><td>0.6.0</td></tr><tr><td>IBM Q Provider</td><td>0.3.2</td></tr><tr><th>System information</th></tr><tr><td>Python</td><td>3.7.4 (default, Jul 16 2019, 07:12:58) \n",
-       "[GCC 9.1.0]</td></tr><tr><td>OS</td><td>Linux</td></tr><tr><td>CPUs</td><td>4</td></tr><tr><td>Memory (Gb)</td><td>15.560554504394531</td></tr><tr><td colspan='2'>Sat Sep 21 10:57:13 2019 CEST</td></tr></table>"
+       "<h3>Version Information</h3><table><tr><th>Qiskit Software</th><th>Version</th></tr><tr><td>Qiskit</td><td>0.14.0</td></tr><tr><td>Terra</td><td>0.11.0</td></tr><tr><td>Aer</td><td>0.3.4</td></tr><tr><td>Ignis</td><td>0.2.0</td></tr><tr><td>Aqua</td><td>0.6.1</td></tr><tr><td>IBM Q Provider</td><td>0.4.4</td></tr><tr><th>System information</th></tr><tr><td>Python</td><td>3.7.5 (default, Oct 25 2019, 10:52:18) \n",
+       "[Clang 4.0.1 (tags/RELEASE_401/final)]</td></tr><tr><td>OS</td><td>Darwin</td></tr><tr><td>CPUs</td><td>4</td></tr><tr><td>Memory (Gb)</td><td>16.0</td></tr><tr><td colspan='2'>Thu Dec 12 12:53:59 2019 EST</td></tr></table>"
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -521,6 +528,13 @@
     "%qiskit_version_table\n",
     "%qiskit_copyright"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
@@ -539,7 +553,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.3"
+   "version": "3.7.5"
   },
   "varInspector": {
    "cols": {
diff --git a/qiskit/advanced/aqua/finance/simulation/european_put_option_pricing.ipynb b/qiskit/advanced/aqua/finance/simulation/european_put_option_pricing.ipynb
index 920cb7da4..84a81486f 100644
--- a/qiskit/advanced/aqua/finance/simulation/european_put_option_pricing.ipynb
+++ b/qiskit/advanced/aqua/finance/simulation/european_put_option_pricing.ipynb
@@ -1,25 +1,22 @@
 {
  "cells": [
   {
+   "attachments": {
+    "qiskit_header.png": {
+     "image/png": 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K7KdEZsu7QvYk/wuyZbahTeNE7Q/YTF8+gXY5+vSriPjkPZAYM/2yHpv9pM2pQ5svlCDWSeTlgxgzkXdQVLYP0OGiDNpS7mHR5m44iMn2bRZ5x+Xg0dmSbMPuI8d60RtCf8Fm1tofM8FFvh+YEM1nYkBKspxfaq3Y7HgsML9bkPMsFsT6C3kjQIzxyDsnJttkdKgOg3ZNNCHidR2IyXasGXnlavDobEm28iIWOYYSW8Fl2MJcvW8QBS5JOIuIL1aBdNR5ZTVmbLE/hgKYqUcO+54CRLqPvFQQIxt518Vk+zY6/AAdyLfgZT8Qk+3gJuTd8gPPzpZkqz5iL+JBOHD8t5kc4Vb9OFYDLvCe04RovZwEEgmd83utI1r9Fh1wAm6yyDmrBZFeSZLtYzHZLkGHUuhI4RkfEJPtgBrkPQoFz86WZEsl1iLH+ElLg4X8/clUe2nJABq6FPcbItbukIMEvIfuuNNkwVa3Z7SEusVoTySTAnEskmRbLyJbYXjDpTrdIuIp8qriwMOzJdkqStGmfjg0U/T72oCtrPrKX4tju0qFKahGxOvDoLs0mesv1hpZbFX3aT8FNMuoRpuNDIjijZJki7SIbA+gQ6RE2QZUIK8+FTw9W5Ktz2l7n5fU/PdMuog8s/7Jb+8mqaEzfb9GxIbO1nm97KAzutFbymsNVnQwnRingVbMSQtyrvqDKOESZesnIttf0WG0NNmqypFnGA6eni3JlkqpRY5hLfDogOK/UcBseHVh5+S+jHOIArLRTxGxYmLn1ToDIW3avLKKemGzaD47zY8G3vsGe8pDaRAjQaJso0Vk+xk6LJEm2/3Is0wCj8+WZKtYhDav0kBIETT7vAUFrMbGJye3FQzwea3BPnZeoTtt6Zf5dZVt89V2wH3LFi8dkvXe11erDSYWBeoP5vvLQah/JdqsYcS1J1G2qSKyXYMOP0qS7VIUmAMkW5Kt9xF7lReU4EyunfBzDTqxGJpe/v398vGxPv/5Tx8bPkQq6w4iPpwF7XOq3BH7fxQhg2fvPH6/1mC2opNHe4ZpZeCMPmpBznkNiDFcomwzRGQ7Hx0aEyXIdpwVne7fJFuSbUwlcgzroQ3aJ3X1X0Z0xpr1jVVXftsya2hfb8rLzl5iwCoroulYaFfZetlRqpCMgmXfnv2n1mC0sOis+khxtIoGAeEouS6FAhFSJcq2n4hshyDvH/9uZxtf99peSZItyTbfXk39MGgP4z9qx3UTvo61GAx1z68f+WL1rJyUcB/aNtZNvICIzxZBJ7jLvEMTRsxY9slvfz2u1evNFmyj+vfSFI0C2pNcgzbz5CBCjETZhorIFp4h71xMN7PVViDvayDZkmxb/5ewN7XQLkrGhI/bfKkO28GaTQZ9Y82jmxd//XLr0tKfWETLpTRfH0ZBQTOaYbx9A0P7xqWOnFT0wYZPDpy+8ehVg95gMrPYnsc/l2QEKGQUtEt9yYqc75UgQqBE2fqIyXYPClj2xXcnW+oo8o7SJFsOyZa+jBzTPmiLVqo1Go2PWqkKHvzuj3easAOs1WwyGgwm5BgfXf/70oVTx+1OnLl06a8bFQ+f19Q3NDUZjEazhcWOvDq3dXKcVqlS+2i0GrVS5gVt7DIi504QiKCQJlsriMk2A51Yz64fpXE32y0ocEVGsuWQbCNqkKOfC69hvAN0QUGBfhqtj1qtZOTqiGEL912vwy6xVqvV0srKYbEr5qendxQm+zMKRqX29tFo/AKCgoICNAwFziY3IccwmAIRDJJkWycqW/gOX2e9+tWHeTG06Gyno5PlJFsOyXacvaraJBDwUgWERUZGhAUHBQZw3Wq8vVUMw8hpRpcybdPhe3UoHePzP75amBOjkdMKhlHaotVqfAMCg4LDIiL7hgeqKBCIeoY24o6UqpQk2yfisg2qwXYZrn6xIIMRkW2qHp0Y+pFsSbYAq5pvBVq+WSYwOr5fbEwU160uKMBfq9VqfLzVKiVD0wAyRh2YMmn5vvNPGljsFv2La79tmzs8WssoZF5AyRS28bGtWj//wCBdWEREVGxs//gYnQocmAtW5HzCgAh3kZcNYoxC3i1x2cI0FjtkOv1usIvZ6h7ja/6gSLYkW/gNOeYfoQWliUpKSU4cOCA2NioiPITrNsDfV+utUshoaN3ZRMkYpXd4+qSSHT9fvFtjFJmvqfaf8oMfL56aHe2vZmQ0/wYikClUaq3WPyAgSBcabq92YEJyalIsv1fqEyNyLmpBhD+RNxHEmI68SyKzhXksdoK9UEi7kG15ObbxAcmWZKu6KzzTXB6WnDlkUHpaStJA7n7bNzwiXBekVStkjmIFKJmcUWlC4gZNmLNs+76jf9x6WCncUCxkNdS9eHznr9/3f7Rm3qTMfmFaNWMLludol5LJ1dogXTh3r42J7TcwISUtfVBmZkq4HOzm6pHzrC+IcBR5c0CMEuT9JDZbeNeKnaqYTnWRbfuaokm2Hp9tTANymsbZo43MzMkZMSxrSHpacuKAeO5+GxqgllPgXGx7+dpmpb5hcYkZ2bmTp89774OV6+3WLil5r3hG/ujsQUlxEf4+ahXDyPlc22cbL6v9Q6Ni4/rbqx2SNXRETm5mtAI4Q2qRY8miwXUbkbfY7cPjVorOFrLvY+fKw93JFk+TbD0+29EscirjAGR9c96aOG5U7sgRQ7luU1MGRgWqbLdZEIGiKa8+IJMpmBYKhVwmk9EUBWJ4Aa0K7DswOTU9Y0j2iJG5o8ZNnJwTLQMIe8gip0gOrstH3mYQYw/y8sRnC6rdJuxU1UgXs31exCJvHsnW07MtQQ573RvCxr1dWJD/1oRxuVy4wwYPCPGhwQvE8qLkcpWPigK38eV6B/fPGMZFmzs2b1J+QeHMcZFAN69JbWDAdVHI+wrE2I+8cBHZ8kI312BnLONdyrZ6IOxFXl04ydbDs92DHPMvqhHvvlNcNLNwSv7EvLwxWXEad8OjlT6+umA1SMM7asjovLwJ+QUzZs4ufufdXNWXJuTsV4EIgnJugBj/CMIBUdny1IV772DH6vu7kG1DBoDvC+QdIdl6eLZH7dmWTV9aurBkfnHR2zOmT84MlYP7VIFhUf3DFCARefCgidNnvF1UvKBkYenSwr1G5JR7gwin0IENcPMd7E+Jz5YXMHFbuQnbd1/ZZbaGkcCZhgKFJFuPzlZxFTmW31esXLZk8fvvLJg/MzMIukUWHJ80ZFAISMcvY/q8+e8uLF2ybOWKg2bkPAoGEbYi7y1wXSHydriXLU+VPX/3iccstlHaVbbmCW2fT1KtI9l6crb+j5Fj/nX9hrWrlixdWpIbBN1E+aUNHf3WEA1IyDe7ZOnS5avXbVi/39wy1xNhKvJ2g+v2Iu9t97MV8h5Ssq8anVR5d54tW9je08B+Itl6crYRjc2D5O3bNq/fuG5qGHSfKnnitOLiZBqkFFKwZtOmLdt3fG9EjjGTcnNN6rp7ByyzkdJkayMbdRaFFnSebQm0WoIC+SRbD842iUWOoezT3bs+WplGgQTo8JnvLds0LwKklbzs448++eyHRrQZR7v7XIIMt47FuAASZMvLb0TewU6zXQEO8uvIq/Qj2Xputjlo0/jL119/+24gSEPz9sadX32TrwBp+RV/8+2+n2rRplDu7m6nMrd2V82XNlsYjrx6eSfZbgOBwVbk7SPZem62+WhTf/jnsqlykAg9as+Bo5e/HQQSoyft/+VwDdosVIAIASZ0sEaDawaw6GDylzhb+BZ5KR1newycfIwCY0m2HpvtbLSpO3V0KkgnduOl68+q1mpBahOPnmrOdjkDYvyKvI/ANV8h7yBInW1QnbA/V5+4p32OvCcakq2nZvse2jSULwAJyd6+2mDGCzkgudmXatFmKyMud+Tp48AViUbkTZE8WziBDjNdzhbyUeBzkq2nZrscbZq+VIKU+h2w9bHZD6Qm/6gRbbaJy1b+CnlXFNA15S3k1Sulz1bQZqnr2cIh5LHDSbYemu0ytKkZBpKiZt5HxL/zQHKpL9DmS6XoSHjboWufosA2kD7bYnSYLyLbiEbkPVCRbD04W+sVNUgr7HMzonlPMEiNOW9Fzrcis9XVIo8dJWpUjZUad7IdUa7renZiN0lEtrAIBXaSbD0z2+3IsRwGqb11DRFvTwHJlZndyRZKUKBuOHRuTCMKzAI3si0y4TVf6Ng+dBgsJlv6CvKsg0m2HpntOnu25yiQWOAOA6L16yiQ2jGLW9nSV1HAOAU6U2RGgXJwI9sNXb3Hw210iBKTLaRZkHebIdl6YrZL0eZFAkht1CVEfDSbBmnFPEObr5Ug0mAWBdj3oWMrUIjNEJ8tcwDtTjHQAa0VW9XIRGULu1BgA8nWc5ekTJ+B1DTrGhFxf3+Q1sdGtNnOgFhfopNjA6B9aWfQydcgOtvAS9jiRiK07wd02AfisvV5jDxzMsnWA7NdgnYNM0Bq2acR8XmJHKQ0vRHttonPNuAlOrHs1UFbkd+z6KTCT3S28Q/QwbiYgnaUIm+yyGxhAgpclbmT7TmSba/Odh42q8wDiSmX1iDi4XSQ0LhKbLaaAdHSG9BZw7cFGueyCw8Y0FllXxCbLV2BQmdioI08CzoYvMVmC7+gwAp3sv2CZNursy3EFlVTQWJphxCxZokSJFP0ClssUoB4Iwz4OtPJJTNyEoIUusTcwpUXrPi6hhQQnS0k1qAQe6pQCUK5Z1FgG4jONrQeecb+bmRbig5ly3lToFcg2eZhK/1aNUhK/n4lIp7MBolodxmx1Sw5uGGSBcUxjwI3soX0enRW9/nUrEg5AKjic+ddRqHnPuKzhRIUKKfEZzse23UMegWS7RB0sJzMAEkNKEPExnUakERuuRUd8mTgjtkozixwK1vIbsK22Mqbr7CNQnAjW6ocBRaJzzaWZNurs40zIa9mUyhIiJrzEBEv5YIE4j83IM+cTYNbSszoutrJ4Ga2kGtE15SBO9lCkhl5TTGis4U/Sba9OdvgKhRg7y0KAulEfW1FNGwPgO6KWP2CdQoqiQL3ZNxDV/3ZF9zOFka/RFcclLuXLWxFgTPis023kmx7cbbeFejEWvFhOEhm2h1EvDYRumfgtqdWdPI4HNyl3ouu2aWAbmQLuoPYtRMMuJmt6iEKzBedLXxOsu3F2cJ55FiuPcZW7LM9GTRII+QTM6L5szBwnzp33ysWW909bUbOVR9w38SX2LUXEwDcypY3ux47x+5WgrvZwlgUqA8Xna1/Jcm2F2f7A3JMXw44jjzjqbkRIIm8K/ZHy4GbqITlf5mQtz96txE5B1XQDbrttdi5+wuU0O1sIfJs579INoD72cJ+597EZgtBB0i2vTfbVcixnAPlgofIYyt/nBIM3ee/1YDI7osGN8jiSk7Uo8C1KTI4ZUHODga6RTX3GnasPJ8CkCBboAo7/mVerFdBt7LV1aLATJHZ2kx4RrLtrdlOsVf6UAcQuv6RkRWU+7xs3gAfRgbdMvwci/hkgRzEoBTK4BFrL9Sz6GA13FjkDxB4l0XOAgV0V3ZZI7bDenVPNvDczJaX83MDtuPMFAVA97KFeShQoxOfLWjyFn95sZpk2wuzTTMhp3aIrZb47Q8NVhR4eXptbpiKocFt3itqEPFgshfHxWQZbdrcb+6YUMBiuF4aApykantbOTR0H9VvxvYztcgznFk/SgNSU4zcfqYSeYY/dk2PBHcRJFt+aUI/G+xiV9/Sm1HI8vC3Zbl91YycgnZ5CUFbg46yiFWlSq/XQVsyRhmUPnfvX3UsCrCmpgslgWA3XY+cV3EgmfHo8Dn0GN+M0flFJXMmj0yNlgPRXSRb+hxyjLuhRdCsYzUGKzqxPP5964xUHdcuLYiV+6yPF6cPp+MUmUVViHgik7vOcWGfNvVSNKP07TduedmdJnRmMTzb/5YGWmw1IudvX5BMqjDb3oEg2cKn9jjOyPmQ0zdcaTSy+JrGO4e3zMyO0KpsN94+LcV6dd1t4k8sYu0aHz7bPvxroQ/QCqVPcMpby74rr2axTbOvTi/qJ4NW9DELcvYpSbaejWQ7C22eRICAduyntxqNVmzD8Kz8+7Uzh/ULUMplMrCV2GW2suLHiHguB/ibLfeK//TxomkZ4xc7ZMqyL049qLPi61iz4eXZ5RkMCAQ/spe9UEGy9Wwk20QDcprywFnA6J3ltQYzi+2wvrxz5uuVc8alxgR6q5QKhZymKQBhtEJx+6yI+i0BtqopSqZQMEq1Ljpl7OxVe4/feG7E9liM+keHPkhlwFluA3LMOTTJ1rORbL0fIMewCdpQp5X+9qDROV0h1vjy9sXDX20qnTVpZHq/0CA/jVqlUioZjqIVwzDKwgpEvDItKCw2dVhe0aJNXx2+eLtKz2IHrEb9y4t7Cvq20+YqA3IehYNnZ0uQbGF/84YLBbSDCh21+sj9JoPZip0yNdVUPrh26fTBn775YufmDcsXlTb7cO2Wzbt/eWyLsfJ+ZXWDkcXOsGajvrL805mJKmiP/IQFOYdUnp4tQbItQZsX/aEdXgC0f+KEVfuvVjYZTFYWe4zVYtRXVxzbNTc7xp+hwAvaEfUMbZYqQDopJFuiN2aboUeOfh60RfmERPYN1/lptCFp+at+KH9arzeYLdLGy3LBGhpf3jqya/bQ2OCAwLCo2Pi4MC0NbRXqkWPMpkE6gSRbojdm6/83csxl8Dp5YMzA/rHREeGhQUH+PoxCGRibW7y57OLD2kaD0WTp5r2XZS1cr031T/48uHvhxJQwrcrHLygkIjI6Nn5AQkpy/xAlBa/53oScm0EgpVfY6tJCgfFAEG9wtswOe0VPIkGIUoUnpackDIyLjYmMCNUFBfhrtSoF9PECJiRpwvvb9l+4/ayuUW/rV0zArNViMRkN+sa6Z7cv/LxncUF6hDcFXn1opdYvKCg0PCIqNm5AQnJa+uCsjBgfGoR091nkfMyAlC5gu34BgniDs6XHWpCjLwIBdezgYZkZ6SmJAwfExUaFhwUHaFVyCgRobUzGmKLlu/YdvnDjEbfa1NikNxi4ik1ms4VnNptNHINB36RvaqitfHLj/KF9O5cVjcmI8ZWDEC1TB4RExtqqTUrPGJI1dGTuiIHewnALmuzhT5SBlPaSbIlemC2EVCDH/AufiDxq5LjcnGFZQzLSkxMGxMWE+avl0CHGJyg6eei4KUULV2ze8cXXPx86cupcizPHDh0q+2bvrk0r3p8zNW9EalywDwMd8pKrdVEDElPSMzKzR+bmjs2bPLafGhx+MCPnQRhIaibJluiN2TIfo83zeGgmC86ZUjBxbG7uyKFZg9KT40PUNIhDyVvQjiL/83//BzLaC7oi84lIGJw9PCd37PhJBQUzZ02IlEOzqCcscvYqQVLUnyRbohdmS482IcewBOyY1KLimVMLJueNG507YnC/QAVIwItWqDUB/gy4QBGcOHzM+An5BdNnFs0rmT/Uuzn2EgNyLJNkIK00K8mW6H3ZQuA15FjLfWx9+U4sfX/+vKJCLtwJI/trQCKU0i8oNEonB5doE0blz+CiXfDeBx8unxpKA4DyrAU5t4NBap+QbIlemC2zFh37kkPmrl7y4aL35hXPmTkp0Rsk48XoouISEwPBRT4Jk4sXvL/ow6UrV298P4oGGFGHNlsYkBqzwUSyJXpdtlRKLXLMZTSEfLB1w9pVS5eULpybqQUpyYIGpAwZmaoAV2mGFH+4YvXajZu37l4RDdRX9rYahlAgvcRyki3R27IF1c9oU5WsWfzZ7u1bNm9Yt7wgDCTmmzRibH5BMLguZPLqzVt37P7k86/W+cU+RZsTPtATqGnbDlaYSLZEb8pWNsmMHOMnM3/86vPPPtmza02WHKQm7zehsPiDMWK6k/8/e/fh0NS993H8H3nuvf1mgmAAARdOcNGiRdy7tigWtO7Zwa3bqrUW9x5VXGC0roqrqFVcOCpOlkzDDPOQ9XnOyCEhhhDUXPTe8+o+g5P8ct4c8supDl63/7fEI0nHTy/c3gCWeaaSPEbRI8ymM0kkH3a21OEBONVXTp86eSLp6K99yAOCv1qyetOm/gpqhZ5rjp88dfrMuT8rwXnekQQSiZStKt4MliXn0uVLF85v6UqeoByyZteh86taV16nX1MupV65km0By7JKTRKJlK1Vl2xwmIe3b9082p08o+vikxfTH072otbovC/t9t2HdeAU9CaRRCJlq14jXM7KHj+6PZY8RBn9x5OC+vNh1CqRqRlPXguPbquaJBIp20bdcsExFeUekJOn9Nhb2oDauT7UKgnZhQ3g6PqRRCJla6NaZgKHKYklj5FNfQ4g7TNqldG59eCYN6pJIpGytRPyHLyqSPKcjjuNgGGlP7VG/zzwXoWSPYlEylb1gwGcuq/Ig8bnA3gcJaNWGKEDx/iTmiQSKdsmgu+C07BPSZ6jSWEAy4Zgcp/i12pwMrpSUxKJlK0irh6coijyoOnZALLHKMhtEZlmsJh5SpJIpGwd+J62gGW6HkKeozleB2B/CLmr83kGnCv+5EgikbKVRb4Gx/hHiJw8ZuIzC6CLVpNb5CFJ9eCUj5bTe7IrOfkr+oCMWXssOTmSWiMxOXkUuUO5IGm2jDhHkpOHk6OOycnJIcQZeXB1MP2PGfszO/Cff+zZknqtQej2wgA1eYrX/ioAx3uRO9SfWa+1xu1qegvB3ZroSpwCYAV9OJLAiaPWqAYWkjsmAxhNHAaYQ45CAXxGrHY1wF7635IMztcffbbU6RZ45owpGhl5yNBHFkA/w5taJNdMyzCBl9GN3kYamnj9AWY7E57MNgHAcjeyjQBwj/6nzMJ/S7byCZUQVO0IbUeeodhYDuBSP2qJz6f7qiGonqL46LJd+uDBTGrZVWDToEGD/DyTbRSAfm5kKysC1tBHoPuDB3/Qe/EXsNHlwD948EDxUWRL6s0NEJif/BDioXDD75iA+u/bkysyn57LMk0QGA+r6W2zNTM2r/6T2e5y7zD5wLdk856zpV5zu5Eb2ZJm+lD6GPQFcui9KGxpDAGoPo5sqWOaBVam9G+7tpORByx/DeBOBDVP0T50WYYRor8H0Ftne5XsfXjZ6j2arY3LbFvBb8a8eR3+G7Kt/i/KVj68CI1Md37sr1HRe9ct1QAYVwZQM9QBkeufG9GoLFb9EWa7978y26n1ACKpzYT/x7JVfETZkiq+DjamrB3jgn0U9J4tygfwZKharXDSrKbntGSdBTbMr+3Jo9mqyYl2dutlJHK5i6zJ0keOh/F2L1uFwv1TTi1762y9W8hWriZH3jKii3Carasfy5RKcpMPiZodt0VOs3X/9XM/2+Fvk626jbIln0QD7FXfWDU0yEdJ71PwOQawHIiLHhPWQdXkvNeETNr51Ah7xvOdyXPZDtyUWmF6emyRkqy0Wm0U9fy9EjU3pxKrd3KRpS59nZIaef87+aW59EpCH7KaqdUmkCr+hcGceTKUeLtP5gJ4zH6xL4mjWXu/HOw+46mJg1qtAbjPbmYtYcr+dKY+ff9kEiVqtWOI+i757egbp1yvAzpU/7W5Owm82a8SToJorXY3cXqxCwPfyNZ7VYoO+gsj7bLdp9VOJM4PWu0ydoMco/HZsS7UyPfXGzqY8hL11+Lj44PInmbJIz2YlxeiyYmIXXcZc/7mYJrBDRJnsla7k6z2iuNDFLYnh4H+0dFQsurMPvQQirqsR9k16yOfpL1pBGrYFTuItUOrFQeqP7vMl1gyfix7n65EzY04YoUeL7LU3lurIJE4rMbGge/J/kN8Sgu12lVENFB7uR7A7+waOREt02rjSeDHLgojznatdgpRj/i9WrWwZvmpHEtxys8hbZEthaSZ0YSl8uqqkZ00ahm9N3GZFsDUYKzLPTreX0Y8RbuAPrG7Mxg0ZcmIkHsu2xVGCO6Jgw1gWpdy8I6rKNoAXnpnsur/HIK6eSTYAdynA+DVziJOHkTLifWVHlaJarJTAVEcsdqfgFWyr93FWHnEDNQ7ZjtcD17ZCGs9ACaQYBWQT5zBALo5Ztv1b/Asi23ZvgaWEucEkEJ/gFfRWGKcDlZnyMGIUlilhZCj6Qx4+aHbgQfEWWN3vSwClhHvJzMExr3yxnexCI8ygnfah98Iomxi5dhmv8cC6CD+aBsbUglekpKmGMG705Hs6SH6migSQA8SHAFSiWgSGimJKAU4QYJOAIYTJwv4mXYYAfgQa0g+BJWT2iJbWVQuHFmq722fOiDwvaXre6IOVrq1ITJSevt3HfrjqTwT3lAYrSZPZau8woV2+2yWBaiMasx25h0w+Uaw1vVrgCm/DqzTJIitByzZZ+/UADhoy3YaUKYDyxj6ZrZhVYD5yVUd33/HZrPtkQtWQSFY2SG2bI8Db2a7VAe81oNlmNK6bHvrwGLyTbDMdJ7tYkBXBlZNEPE+NwLlSQl7ud5/dKjWCKAhPcsA4K6cmloGTkkZ8HCHy2w3g1V+txisNbZsR+WDyTOCtaEV2U5P558da024QXz9Tnok2x2ANdvvjID5xen0egC/tEG2pIgrgxPmwtSEaeEd/bxV795uYHINRExCaI+R3yWm6+GM/od25LFs4wHzFhURDS8AHjdmm/N4nIzUCxmg5nrJXDXJhr4CLAOFOMqAjHAi8kkCMEbMtrQyIZgo8Ih4OeoXEaEDdkdERAQR0SngVV8i6rnKiOs+ZBMeEVEDbGQ30xDReYBZpSHSrGGA043nVxJQlH7xjEO2RaXxPkRdd1uAXEWrsk0GcHWAnJTRGUVOsy3Qr/In6vIHxDunlJnAFV9ifQuYIsneQyBtgjdR8AmIDTYKqAUaVgYRdfilrshVtiFGYM9AGVvQM8DYrzHbnL+GEam+0QN1nYgoOCJiP1DIDldfV9nmPOJev28ZoOqGbraKZMPzAcun1HTga4EEYeCdZKuJiFgGIJLdgFxkqzVBd//SBTVRVwa43YOIOlwAzP3bIFtSLa1ubNXSNN2SW4k/TAgL9vNVy+ntKVbo7Mu886LK4TAGWNVtbE/vlu3DUTYO2bYvAxYTr7cBmNmYrT9xlgAwCLH2tQDbiLMJyFQT7yzwUMwW84l3CbB42x1GkAfEEm/cCXWzU1IjActw4o0GMExcjWv2oYjZ6vsTby6Ama3JtrsROEo83wyn2eLLxiL1xBkIIJh414B1ZK97VroPceQZb3yPTADMw4g3weQqW4phNhMvrAGIb8z2pkpYC3GhbW8X2WZpiLMcQIMQaziATc1MSTnNljVFnJJykS3SR9huUb0jI95d4GJbZEteW+vAM+kKqwxwwBTcPrI6NrJnkL+vl1ouo1aT971nRrMsTMUL8R0ukxhI75itPYeefgEy5LaPazLFbDcQrx1ju+HvuvUqGswA40jQ0wJMtGZbI7f11s8xWy8Ag1r+ACgdSCKrE8BtcfUjNTnJ9qDdc8xoTba7gcoAEkx3mm2e3Vp+ywVAIQk2v3FOBvmTYAWgI3tqPZBIVjddZkuhZHUbSGzMdoYt0J3uZ7ueeL4G4JZtkH73QLa5viToa4ElnATDAHzaFtmS5jAjZPtk8dpruuoGJ3FVZt5IWr9wUmRo18AAjY+3WiWXkysyuVLt5dM+ILBz6KANOjTDzFRmn1k07Z5RqPZsV/JgttfEQllx4mUSwHQSZANHSLAP+JtYE4E6GVkVAmus2T4kQTCAaMdsSbxz0GW2ajMwiaxiAaNSWO1s12pxJ9Z3gEnhZraOZ3Afp9mKXYaLn/UkAM9sX/kJOTcFQDvHT4VHk9VOl9naHAOuN2Yrnv0XgBT3s40jQZ7tu8ZB4L4Hst1NVguAXLKSNwBz2yRbCjpt7fbhCM3wdVfyK+tNcMaoL3iYqt3508K4L4Z9GhrSMTgoIMBfo9G05/i2Z//NLyAgMDi4a6/+g8fFzFm6MflS+qtKI5yxNFSXZiTP79suylptw5+hsnfOtvaljUNPBbZC6VMA4eJMsu0CcZgEa6wdLBYL5fwFJDXOJFsZgHlvZHsGMO6NUrnOtr94oRaLCRNXu/7McZRQptvZ5gPryaq5mWRBAICJ1u8MVSTYD5wg5yYCaE92xgPoTFbb3cz2AHDTNpNMgt+Au+5nG2u7cB8kwS9Argey/ZmstgIpJMoENrVNttTlnNCtOTO2HXmHLzj6SKdnTGiWhdGXFGQ9TEs9d/Lo/j1b1vMSduzff/jE2Ss30l/kv66sc7W7oa4iN3VDdGcZeUc/FzY03OgjVuuRKSlvC5D/wiobQJxDtldt2a60drAfqH0hqgHuOWZb7yzb7pVgGV4mze7cfLZfA9CQVSCAaPey7QtgpPvZKkzA9+5l217MNhzAKOJ4Zb8ZGo34YcPR8ykp6Y7ZLgKgcDdbr9hl20+mpKTkO8t2z1tle8OW7RoPZ3se0L8QMcDZNsqWnUhkhKCKF7YnIlnQiGXJD4sr6xrMeO9qy3Ku7Z7VV02s9nPzLeA03I6Qkyez7QsH8S1n+yeaeuVWtjTkGQTGrd7NZbvCPtsAAEvcz3am+9l2BfBdK7OlVKA4htv+MlDoT00seA6RQ7YJAOTuZavZUgHRx5jtEzR1u62ypc7WblH5S6CMeAGD5+++9EJXWcMYLXh/6g/GiP+boMz/p3LwmJtitZ7KNgiAdr2dyJazPQ7krrez2L1sST79WA54qc1lO9cx25nuZ/ul+9n6vE22vqkAmOxyAOZRJBJHBob7F09otTcds13qdrbKGwAqbp3VarXZH2W2fwEZ6+3Mb7NsqfPZemtWSb1UJFJ1GTZv07n0vFIuXvPb1euw15NPyUrZI7HOWu2lPjLyXLZiMV+TI9fZ/gzcIXtuZCvSRC3PBTC1mWyHAAghq54APncv288B9LNm+0XL2VI5sKy12ZLyGASOvzb9EAuQFOj8ve0UcYFjtrmO2W4ETAtUxDnQqmzXvq9se7aU7UmX2R4ATpO9tsuWgrW14BlujmxHTfiFTfh+S/L1jDxdub6mjjGa3OnXYjY21Nfoy3V5OgNsGjYGk8B72PUG8OrOhMrI09neB9a0MttpQMVbZCvy0QMHmsk2AMAY+9kcjXvZzrDepqO2ALPdyPYhkNjqbAen6PccO7v7u2HkYB2gk5HzbCMADHoj26VAtWO2j8TH1KpsnwB73ke2AwBEucz2d+C6y2yXAk8/lGwpeF8VeOa8b/2cZKQO7DcyLn5j4umrD14W6MrKK/RVVbW1dUw90yBgGKa+trZar68oLyspznmclnJky5Jvxgz4+m8TRMa0gTLiyDTzss3gVR/qSuTxbI8BqST6zK1sw+2newNC3M1WTlaHgNRmsiUdsJJsp1khucz2V7LaD5QQp9h2Gu1xke1p4KmMBKPdzDYRRRpy6gKQ6nhxFflZ7G6F/F3MNgaAP/HUjJCtygD8QIJj7mebYnuos94l23Z2r/mfTrPdZDvqIKfZTgAagsmqa4e2zZZ8/51t7av+1AgNNUvt3yNy7MTJsbMW/bD8p4Qt2347xDu4a8uWX1cv/2HBjNhJE0YN+ax7ezkJYm6KN0bp/xyuIo7P8OR68MyvVmjoP5BtuBn4Uhx34wl1C9mKy66TwPueLtK9bL/KH2b7JGNfc9kuBSoDiRdcBfzbdbbP1MTrYwA2iU82Uy7ejNh8thMAzCfBYfey7Q/sJedOAX+TYLtjtnQOKPMjXlCFmG04Gu9N+xJCtrI624dSWe5nuwNgOhLv9LtkSzrgMvH8apxmuwDAEOKtd5qt7AmQTIKAzNywts2WvKY9YMAzPvm+m5Kc+0QV2DusW7Cft0JBn/zrH//8178++eQTIvZv//rXP//5j39+QkovTWDX0L6d231CPNmII09Lq+tqSp7vjVARS9555iMjeMzfs73ovWWbFmwTRJxXjT0dBcrjiDO2FkhoKVsx9WQNsQIuAmVezWeb23iYBWYw3xCnZ6WYjJNsVTnA3VBihaUDmUrX2ZovdiLWkFygXnheGwBsU3C5/W56M9taMVt6BNTwD8J7g9m9bMcBzIIhPbp3795FQU2tBUzCCT2p7o1sBwG4LzyjR2YxW3UJUDqYWJGlFiFbegBk8bvK9xpcZ7vSLtsYABc0RKRca3qnbE+L03TBt02N2UbbZdvHDDwOIdaMBqfZ0ngAu7yI1Yl78WRtmy2pRlyohEB/ZESA82q9eg4dMnAAe7eFf/t2ShnXK4uEbj8hmdLL1y+wa+/+EVHDBnhZu6WAib8kJiWuGeNDHM3gnUXiYa6MU9G7cf1LwF0DSjMzOxNRZz2Ae/tWnCoD8ErjTraUCEB36udtf1sghuA821T+MJm9iMJyARSfiv9+fy2Q6dNctjTZAjAXN2+5xACWr8h1trtMTMq2fWkmNN73p6kA8OLQxhNV9U5+SH4C6DIzvbjDgPXk2MaTOmxzL1tfI0Tm4ssjyE5fBmBSl8YsvoFV6UBWZgjZuQyg/tqO3anmiiNitvQdAOPlbbvTcFx8b7sEQOnxmd+syzNPMKI+81Kz2X4NGNhRXS3eDopi7YbDBdjyTtmGGgHc37/lHFN4ojHbPgDYIx0Rv8FDf2bjgWfY4jxbuggg//jqPc8BWMZTG2dLst57Ci3gmZ4tD/WmNynDYmK+GBUV0bdHR39vlVxGYprEp8tm2z6oW5/PokaMnzwlzC5JlZeSeF49Ft1hICg+2EdOHs12GiCezD3uQ/SwO7mVrWyNCVbMImouW7EO4Z1w4B2IqvpTs9nS+BJY6cZSC9kuTIDVXgUJZpsgWOxkSurf4HgTaw4Dwc2+7mVL02pg5zDZWQarjHYzwOpFdnxTILDM3t6YreIWBMUhYraym7DaSacAvGg2W+98cHYTJ1IPwfGx75Qt/QqBKdo2JUU3AYhvsrrkQ3C3RzPZqnZCVBVLbZityO/HpwwEzOVpIao3wu4yI37e9CkTR0eGBnk7TU7GvvcdOPKLydPmfDe3m/yN6jtFayshYJ4t8yfybLY07ykjnszKzfngZC5TkVvZsoalM2DVJw8gV9nSzAzGmi2pN74Gh9kVRC6ypeAzerD0p4OoxWzpx1KwChZSo6hcsEoWkZNsZUteGqzZUtQTMwDmgNrdmWSfFEsFrxacWWQnhj+o8YSGFBvzLdZsRbKEUrDyYsmWLSkSTGBdDyMxW/L6uQ6s8pXc/QKVLrKlnimljdlSyC2w6rcq3jFbiikHKyea7LLtcKSwMVvSnALLnKR5cyZZFP3YyEd7oAd9CNmSatz5UggspYfGdVJQE75fblm/etn3c2IGdlBQM+S+fSbM+m7J6vVbv9Y4rAkauf2VCYKyyxPV9B8WOH7BxH7UKsrP5kwd0p5aQz5o8sKYcB9qUc+42B7kHvXoudHB1ESPmDnDvKllvqMWTAwkd80vYWJI0G5KEXCd7Cn6RC/4shM1p1vczKFyErIVeUfOjenj8IgGfTNntBe1Vsfx88d0oHcn7zN1RqSSXPAfOf+LLuSSOnJ+7GBvog8jW5KFJGQxEKd5t43saJ/nJ903nzp2YNempUN9yQWvfvM37PjtyKkdoU2jjVr3pAECJmdzTxl9aCSfW3CcGk0Cyqn1bNlKPJ6tSB1zocQCgSlry4guahIpR1y/d+vqpeMzfVqa3NpzKTXtXtpYu12Do9b+zcCq9HKcN314JD8Ba+wiBvKkbD+ObEnWbd2TaliZsvd8GSIm5v1Nsa44P+9iOLWg48ZXBcUlr+f5ksCr65iNGQys6l6u7y6jD5BkBXBPbt/fCSnbjyBb8Wp5LI+xfQ5waGpYgIJYvt+ZzSaT4U4YtSBwB2M2m7FUQyy5X2jsgWwDrAyFpyep6IMkiQRwfqCaWL4jkgFMkbL9CLIV+c26XGyElaUiddmI7r5yUk+tB6sw3odcUo/7C6yGeT4k8wkZtvhSmRlWJt1f8Z1l9IGS7AdHX17OgLOEpGw/omxJ1nXJDZ0JIkN20qKobv7DXvLpPZ7agVzwGf47n/er0X4hn889/LIBIlPFvZ/7KenDJVn2EiLDH1+RlO1Hkq1IHrr6ts6IRsyL5PiZV4T6Mlf08aVmqEOmpTLg3Jy+6FBGHRqZyu5vHqSmD5skfO6yzYm71yyYFEBvp8vAgWHUJqRsWYq+P90oYGBjKBbvoqq5NH9AsJMClX6hMYmFEBTlN8CmQXd302AvEkkkUraeIe/1Q0pONZypTUuICe/m5yW3+119Ovcdt+z8awucqMtP/Slc7FwikbL1qOC4Q8/KDHDCXJF+ZOk3Yz8P78saMGhk3OI91wqdbmksf/n7gp4KEkkkUrYe5j0qITVbb4Izllpd1oO069dvpL8sqna+ianq1V9bx/uT50kkUrY2ipDY3TdzKhvQagZ97p1907sryfMkEilbB6qesVv/fF5cY4LbTPW6l1d3TQ9VUVuQSKRsWfKOoxYfvvmySN9gRgssptqSnNvJK8d3UVDbkEikbEWqbuN+3HMp/WVBWbXzeC2GOn1xdsb1Y2vjentR25JIpGxFcr9+4xclHDyf9vB5Zl7h65LSCr2+srREV5SX/SLj3pXk7ctiI7uo6UMhkUjZiuSazv2HR8/+ftnadZt37tq6fu3KH+Z+PW5Qj0DpGvv/7dOxAAAAAMAgf+tJ7CyHYGwLaAtoC9oC2gLagraAtoC2gLagLaAtoC1oC2gLaAvaAtoC2oK2gLaAtqAtoC2gLWgLaAtoC2gL2gLaAtqCtoC2gLagLaAtoC1oC2gLaAvaAtoC2gLagraAtoC2oC2gLaAtaAtoC2gL2gLaAtqCtoC2gLZAy1EI1wwY3vcAAAAASUVORK5CYII="
+    }
+   },
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<img src=\"../../../../../images/qiskit_header.png\" alt=\"Note: In order for images to show up in this jupyter notebook you need to select File => Trusted Notebook\" align=\"middle\">"
+    "![qiskit_header.png](attachment:qiskit_header.png)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# _*Qiskit Finance: Pricing European Put Options*_ \n",
-    "\n",
-    "The latest version of this notebook is available on https://github.com/Qiskit/qiskit-iqx-tutorials.\n",
-    "\n",
-    "***\n",
-    "### Contributors\n",
-    "Stefan Woerner<sup>[1]</sup>, Daniel Egger<sup>[1]</sup>, Shaohan Hu<sup>[1]</sup>, Stephen Wood<sup>[1]</sup>, Marco Pistoia<sup>[1]</sup>\n",
-    "### Affliation\n",
-    "- <sup>[1]</sup>IBMQ"
+    "# Qiskit Finance: Pricing European Put Options"
    ]
   },
   {
@@ -54,15 +51,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
+   "execution_count": 15,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:55:05.347318Z",
+     "start_time": "2019-12-12T17:55:05.338095Z"
+    }
+   },
    "outputs": [],
    "source": [
     "import matplotlib.pyplot as plt\n",
     "%matplotlib inline\n",
     "import numpy as np\n",
     "\n",
-    "from qiskit import BasicAer\n",
+    "from qiskit import Aer\n",
     "from qiskit.aqua.algorithms import AmplitudeEstimation\n",
     "from qiskit.aqua.components.uncertainty_models import LogNormalDistribution\n",
     "from qiskit.aqua.components.uncertainty_problems import UnivariateProblem\n",
@@ -85,8 +87,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
+   "execution_count": 16,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:55:05.813436Z",
+     "start_time": "2019-12-12T17:55:05.799604Z"
+    }
+   },
    "outputs": [],
    "source": [
     "# number of qubits to represent the uncertainty\n",
@@ -115,8 +122,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
+   "execution_count": 17,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:55:06.207523Z",
+     "start_time": "2019-12-12T17:55:05.978995Z"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -174,8 +186,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
+   "execution_count": 18,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:55:06.327063Z",
+     "start_time": "2019-12-12T17:55:06.315226Z"
+    }
+   },
    "outputs": [],
    "source": [
     "# set the strike price (should be within the low and the high value of the uncertainty)\n",
@@ -211,8 +228,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
+   "execution_count": 19,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:55:06.692588Z",
+     "start_time": "2019-12-12T17:55:06.495721Z"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -243,8 +265,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
+   "execution_count": 20,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:55:06.712972Z",
+     "start_time": "2019-12-12T17:55:06.706922Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -272,8 +299,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
+   "execution_count": 21,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:55:06.978093Z",
+     "start_time": "2019-12-12T17:55:06.968098Z"
+    }
+   },
    "outputs": [],
    "source": [
     "# set number of evaluation qubits (=log(samples))\n",
@@ -285,18 +317,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
+   "execution_count": 22,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:55:50.687295Z",
+     "start_time": "2019-12-12T17:55:07.129820Z"
+    }
+   },
    "outputs": [],
    "source": [
     "# result = ae.run(quantum_instance=BasicAer.get_backend('qasm_simulator'), shots=100)\n",
-    "result = ae.run(quantum_instance=BasicAer.get_backend('statevector_simulator'))"
+    "result = ae.run(quantum_instance=Aer.get_backend('statevector_simulator'))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
+   "execution_count": 23,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:55:50.726950Z",
+     "start_time": "2019-12-12T17:55:50.720846Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -316,8 +358,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
+   "execution_count": 24,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:55:51.278677Z",
+     "start_time": "2019-12-12T17:55:50.749253Z"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -380,8 +427,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
+   "execution_count": 25,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:55:51.320498Z",
+     "start_time": "2019-12-12T17:55:51.309208Z"
+    }
+   },
    "outputs": [],
    "source": [
     "# setup piecewise linear objective fcuntion\n",
@@ -412,8 +464,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
+   "execution_count": 26,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:55:51.353251Z",
+     "start_time": "2019-12-12T17:55:51.347481Z"
+    }
+   },
    "outputs": [],
    "source": [
     "# set number of evaluation qubits (=log(samples))\n",
@@ -425,29 +482,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
+   "execution_count": null,
+   "metadata": {
+    "ExecuteTime": {
+     "start_time": "2019-12-12T17:55:08.214Z"
+    }
+   },
    "outputs": [],
    "source": [
     "# result_delta = ae_delta.run(quantum_instance=BasicAer.get_backend('qasm_simulator'), shots=100)\n",
-    "result_delta = ae_delta.run(quantum_instance=BasicAer.get_backend('statevector_simulator'))"
+    "result_delta = ae_delta.run(quantum_instance=Aer.get_backend('statevector_simulator'))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Exact delta:   \t-0.8193\n",
-      "Esimated value:\t-0.8172\n",
-      "Probability:   \t0.9895\n"
-     ]
+   "execution_count": null,
+   "metadata": {
+    "ExecuteTime": {
+     "start_time": "2019-12-12T17:55:08.391Z"
     }
-   ],
+   },
+   "outputs": [],
    "source": [
     "print('Exact delta:   \\t%.4f' % exact_delta)\n",
     "print('Esimated value:\\t%.4f' % -result_delta['estimation'])\n",
@@ -456,22 +511,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
+   "execution_count": null,
+   "metadata": {
+    "ExecuteTime": {
+     "start_time": "2019-12-12T17:55:08.534Z"
     }
-   ],
+   },
+   "outputs": [],
    "source": [
     "# plot estimated values for delta\n",
     "plt.bar(-np.array(result_delta['values']), result_delta['probabilities'], width=0.5/len(result_delta['probabilities']))\n",
@@ -487,40 +533,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2019-08-22T01:56:34.210824Z",
-     "start_time": "2019-08-22T01:56:34.200032Z"
+     "start_time": "2019-12-12T17:55:08.684Z"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<h3>Version Information</h3><table><tr><th>Qiskit Software</th><th>Version</th></tr><tr><td>Qiskit</td><td>None</td></tr><tr><td>Terra</td><td>0.9.0</td></tr><tr><td>Aer</td><td>0.3.0</td></tr><tr><td>Ignis</td><td>0.2.0</td></tr><tr><td>Aqua</td><td>0.5.6</td></tr><tr><td>IBM Q Provider</td><td>0.3.2rc1</td></tr><tr><th>System information</th></tr><tr><td>Python</td><td>3.7.4 (default, Aug 13 2019, 15:17:50) \n",
-       "[Clang 4.0.1 (tags/RELEASE_401/final)]</td></tr><tr><td>OS</td><td>Darwin</td></tr><tr><td>CPUs</td><td>4</td></tr><tr><td>Memory (Gb)</td><td>16.0</td></tr><tr><td colspan='2'>Wed Aug 21 21:56:34 2019 EDT</td></tr></table>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div style='width: 100%; background-color:#d5d9e0;padding-left: 10px; padding-bottom: 10px; padding-right: 10px; padding-top: 5px'><h3>This code is a part of Qiskit</h3><p>&copy; Copyright IBM 2017, 2019.</p><p>This code is licensed under the Apache License, Version 2.0. You may<br>obtain a copy of this license in the LICENSE.txt file in the root directory<br> of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.<p>Any modifications or derivative works of this code must retain this<br>copyright notice, and modified files need to carry a notice indicating<br>that they have been altered from the originals.</p></div>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "import qiskit.tools.jupyter\n",
     "%qiskit_version_table\n",
@@ -551,7 +570,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.4"
+   "version": "3.7.5"
   },
   "varInspector": {
    "cols": {
diff --git a/qiskit/advanced/aqua/finance/simulation/fixed_income_pricing.ipynb b/qiskit/advanced/aqua/finance/simulation/fixed_income_pricing.ipynb
index 1c7ca6f25..81aa37c03 100644
--- a/qiskit/advanced/aqua/finance/simulation/fixed_income_pricing.ipynb
+++ b/qiskit/advanced/aqua/finance/simulation/fixed_income_pricing.ipynb
@@ -1,25 +1,22 @@
 {
  "cells": [
   {
+   "attachments": {
+    "qiskit_header.png": {
+     "image/png": 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"
+    }
+   },
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<img src=\"../../../../../images/qiskit_header.png\" alt=\"Note: In order for images to show up in this jupyter notebook you need to select File => Trusted Notebook\" align=\"middle\">"
+    "![qiskit_header.png](attachment:qiskit_header.png)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# _*Qiskit Finance: Pricing Fixed-Income Assets*_ \n",
-    "\n",
-    "The latest version of this notebook is available on https://github.com/Qiskit/qiskit-iqx-tutorials.\n",
-    "\n",
-    "***\n",
-    "### Contributors\n",
-    "Stefan Woerner<sup>[1]</sup>, Daniel Egger<sup>[1]</sup>, Shaohan Hu<sup>[1]</sup>, Stephen Wood<sup>[1]</sup>, Marco Pistoia<sup>[1]</sup>\n",
-    "### Affliation\n",
-    "- <sup>[1]</sup>IBMQ"
+    "# Qiskit Finance: Pricing Fixed-Income Assets"
    ]
   },
   {
@@ -42,13 +39,18 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:56:11.814929Z",
+     "start_time": "2019-12-12T17:56:08.147303Z"
+    }
+   },
    "outputs": [],
    "source": [
     "import matplotlib.pyplot as plt\n",
     "%matplotlib inline\n",
     "import numpy as np\n",
-    "from qiskit import BasicAer\n",
+    "from qiskit import Aer\n",
     "from qiskit.aqua.algorithms.single_sample.amplitude_estimation.ae import AmplitudeEstimation\n",
     "from qiskit.aqua.components.uncertainty_models import MultivariateNormalDistribution\n",
     "from qiskit.aqua.components.uncertainty_problems import FixedIncomeExpectedValue"
@@ -57,10 +59,15 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:56:11.854771Z",
+     "start_time": "2019-12-12T17:56:11.850399Z"
+    }
+   },
    "outputs": [],
    "source": [
-    "backend = BasicAer.get_backend('statevector_simulator')"
+    "backend = Aer.get_backend('statevector_simulator')"
    ]
   },
   {
@@ -84,7 +91,12 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:56:11.900271Z",
+     "start_time": "2019-12-12T17:56:11.880164Z"
+    }
+   },
    "outputs": [],
    "source": [
     "# can be used in case a principal component analysis has been done to derive the uncertainty model, ignored in this example.\n",
@@ -107,7 +119,12 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:56:12.650547Z",
+     "start_time": "2019-12-12T17:56:11.936344Z"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -151,7 +168,12 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:56:12.900022Z",
+     "start_time": "2019-12-12T17:56:12.693784Z"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -184,7 +206,12 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:56:12.962157Z",
+     "start_time": "2019-12-12T17:56:12.947977Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -211,7 +238,12 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:56:13.018552Z",
+     "start_time": "2019-12-12T17:56:13.007567Z"
+    }
+   },
    "outputs": [],
    "source": [
     "# specify approximation factor\n",
@@ -224,7 +256,12 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:56:13.058151Z",
+     "start_time": "2019-12-12T17:56:13.051013Z"
+    }
+   },
    "outputs": [],
    "source": [
     "# set number of evaluation qubits (samples)\n",
@@ -237,7 +274,12 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:56:46.568002Z",
+     "start_time": "2019-12-12T17:56:13.098654Z"
+    }
+   },
    "outputs": [],
    "source": [
     "# result = ae.run(quantum_instance=LegacySimulators.get_backend('qasm_simulator'), shots=100)\n",
@@ -247,7 +289,12 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:56:46.598707Z",
+     "start_time": "2019-12-12T17:56:46.592800Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -268,7 +315,12 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:56:46.989666Z",
+     "start_time": "2019-12-12T17:56:46.621212Z"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -321,19 +373,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 12,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2019-08-22T01:57:13.085732Z",
-     "start_time": "2019-08-22T01:57:13.075101Z"
+     "end_time": "2019-12-12T17:56:47.138107Z",
+     "start_time": "2019-12-12T17:56:47.016255Z"
     }
    },
    "outputs": [
     {
      "data": {
       "text/html": [
-       "<h3>Version Information</h3><table><tr><th>Qiskit Software</th><th>Version</th></tr><tr><td>Qiskit</td><td>None</td></tr><tr><td>Terra</td><td>0.9.0</td></tr><tr><td>Aer</td><td>0.3.0</td></tr><tr><td>Ignis</td><td>0.2.0</td></tr><tr><td>Aqua</td><td>0.5.6</td></tr><tr><td>IBM Q Provider</td><td>0.3.2rc1</td></tr><tr><th>System information</th></tr><tr><td>Python</td><td>3.7.4 (default, Aug 13 2019, 15:17:50) \n",
-       "[Clang 4.0.1 (tags/RELEASE_401/final)]</td></tr><tr><td>OS</td><td>Darwin</td></tr><tr><td>CPUs</td><td>4</td></tr><tr><td>Memory (Gb)</td><td>16.0</td></tr><tr><td colspan='2'>Wed Aug 21 21:57:13 2019 EDT</td></tr></table>"
+       "<h3>Version Information</h3><table><tr><th>Qiskit Software</th><th>Version</th></tr><tr><td>Qiskit</td><td>0.14.0</td></tr><tr><td>Terra</td><td>0.11.0</td></tr><tr><td>Aer</td><td>0.3.4</td></tr><tr><td>Ignis</td><td>0.2.0</td></tr><tr><td>Aqua</td><td>0.6.1</td></tr><tr><td>IBM Q Provider</td><td>0.4.4</td></tr><tr><th>System information</th></tr><tr><td>Python</td><td>3.7.5 (default, Oct 25 2019, 10:52:18) \n",
+       "[Clang 4.0.1 (tags/RELEASE_401/final)]</td></tr><tr><td>OS</td><td>Darwin</td></tr><tr><td>CPUs</td><td>4</td></tr><tr><td>Memory (Gb)</td><td>16.0</td></tr><tr><td colspan='2'>Thu Dec 12 12:56:47 2019 EST</td></tr></table>"
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -385,7 +437,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.4"
+   "version": "3.7.5"
   },
   "varInspector": {
    "cols": {
diff --git a/qiskit/advanced/aqua/finance/simulation/option_pricing.ipynb b/qiskit/advanced/aqua/finance/simulation/option_pricing.ipynb
index ebae5d431..4f7dcc775 100644
--- a/qiskit/advanced/aqua/finance/simulation/option_pricing.ipynb
+++ b/qiskit/advanced/aqua/finance/simulation/option_pricing.ipynb
@@ -1,26 +1,22 @@
 {
  "cells": [
   {
+   "attachments": {
+    "qiskit_header.png": {
+     "image/png": 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"
+    }
+   },
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<img src=\"../../../../../images/qiskit_header.png\" alt=\"Note: In order for images to show up in this jupyter notebook you need to select File => Trusted Notebook\" align=\"middle\">"
+    "![qiskit_header.png](attachment:qiskit_header.png)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# _*Qiskit Finance: Option Pricing*_ \n",
-    "\n",
-    "The latest version of this notebook is available on https://github.com/Qiskit/qiskit-iqx-tutorials.\n",
-    "\n",
-    "***\n",
-    "### Contributors\n",
-    "Stefan Woerner<sup>[1]</sup>, Daniel Egger<sup>[1]</sup>, Christa Zoufal<sup>[1]</sup>, Shaohan Hu<sup>[1]</sup>, Stephen Wood<sup>[1]</sup>, Marco Pistoia<sup>[1]</sup>\n",
-    "\n",
-    "### Affliation\n",
-    "- <sup>[1]</sup>IBMQ"
+    "# Qiskit Finance: Option Pricing"
    ]
   },
   {
@@ -64,19 +60,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2019-08-22T01:57:32.797978Z",
-     "start_time": "2019-08-22T01:57:32.787666Z"
+     "end_time": "2019-12-12T17:57:42.903632Z",
+     "start_time": "2019-12-12T17:57:40.319538Z"
     }
    },
    "outputs": [
     {
      "data": {
       "text/html": [
-       "<h3>Version Information</h3><table><tr><th>Qiskit Software</th><th>Version</th></tr><tr><td>Qiskit</td><td>None</td></tr><tr><td>Terra</td><td>0.9.0</td></tr><tr><td>Aer</td><td>0.3.0</td></tr><tr><td>Ignis</td><td>0.2.0</td></tr><tr><td>Aqua</td><td>0.5.6</td></tr><tr><td>IBM Q Provider</td><td>0.3.2rc1</td></tr><tr><th>System information</th></tr><tr><td>Python</td><td>3.7.4 (default, Aug 13 2019, 15:17:50) \n",
-       "[Clang 4.0.1 (tags/RELEASE_401/final)]</td></tr><tr><td>OS</td><td>Darwin</td></tr><tr><td>CPUs</td><td>4</td></tr><tr><td>Memory (Gb)</td><td>16.0</td></tr><tr><td colspan='2'>Wed Aug 21 21:57:32 2019 EDT</td></tr></table>"
+       "<h3>Version Information</h3><table><tr><th>Qiskit Software</th><th>Version</th></tr><tr><td>Qiskit</td><td>0.14.0</td></tr><tr><td>Terra</td><td>0.11.0</td></tr><tr><td>Aer</td><td>0.3.4</td></tr><tr><td>Ignis</td><td>0.2.0</td></tr><tr><td>Aqua</td><td>0.6.1</td></tr><tr><td>IBM Q Provider</td><td>0.4.4</td></tr><tr><th>System information</th></tr><tr><td>Python</td><td>3.7.5 (default, Oct 25 2019, 10:52:18) \n",
+       "[Clang 4.0.1 (tags/RELEASE_401/final)]</td></tr><tr><td>OS</td><td>Darwin</td></tr><tr><td>CPUs</td><td>4</td></tr><tr><td>Memory (Gb)</td><td>16.0</td></tr><tr><td colspan='2'>Thu Dec 12 12:57:42 2019 EST</td></tr></table>"
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -128,7 +124,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.4"
+   "version": "3.7.5"
   },
   "varInspector": {
    "cols": {
diff --git a/qiskit/advanced/aqua/generating_random_variates.ipynb b/qiskit/advanced/aqua/generating_random_variates.ipynb
index 2a84095a2..fd1e9b934 100644
--- a/qiskit/advanced/aqua/generating_random_variates.ipynb
+++ b/qiskit/advanced/aqua/generating_random_variates.ipynb
@@ -1,26 +1,22 @@
 {
  "cells": [
   {
+   "attachments": {
+    "qiskit_header.png": {
+     "image/png": 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"
+    }
+   },
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<img src=\"../../../images/qiskit_header.png\" alt=\"Note: In order for images to show up in this jupyter notebook you need to select File => Trusted Notebook\" align=\"middle\">"
+    "![qiskit_header.png](attachment:qiskit_header.png)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# _*Qiskit Aqua: Generating Random Variates*_ \n",
-    "\n",
-    "The latest version of this notebook is available on https://github.com/Qiskit/qiskit-iqx-tutorials.\n",
-    "\n",
-    "***\n",
-    "### Contributors\n",
-    "Albert Akhriev<sup>[1]</sup>, Jakub Marecek<sup>[1]</sup>\n",
-    "\n",
-    "### Affliation\n",
-    "- <sup>[1]</sup>IBMQ"
+    "# Qiskit Aqua: Generating Random Variates"
    ]
   },
   {
@@ -56,8 +52,8 @@
    "execution_count": 1,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2019-08-22T01:39:05.937968Z",
-     "start_time": "2019-08-22T01:39:03.521337Z"
+     "end_time": "2019-12-12T15:16:50.217176Z",
+     "start_time": "2019-12-12T15:16:48.310426Z"
     }
    },
    "outputs": [],
@@ -88,14 +84,14 @@
    "execution_count": 2,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2019-08-22T01:39:06.471867Z",
-     "start_time": "2019-08-22T01:39:05.946725Z"
+     "end_time": "2019-12-12T15:16:50.722382Z",
+     "start_time": "2019-12-12T15:16:50.230860Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 588.756x385.28 with 1 Axes>"
       ]
@@ -144,8 +140,8 @@
    "execution_count": 3,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2019-08-22T01:39:08.718153Z",
-     "start_time": "2019-08-22T01:39:08.706634Z"
+     "end_time": "2019-12-12T15:16:50.763816Z",
+     "start_time": "2019-12-12T15:16:50.753797Z"
     }
    },
    "outputs": [],
@@ -204,8 +200,8 @@
    "execution_count": 4,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2019-08-22T01:39:15.167103Z",
-     "start_time": "2019-08-22T01:39:09.337402Z"
+     "end_time": "2019-12-12T15:16:57.128346Z",
+     "start_time": "2019-12-12T15:16:50.787051Z"
     },
     "scrolled": true
    },
@@ -216,13 +212,13 @@
      "text": [
       "Uniform distribution over floating point numbers:\n",
       "  sample type: <class 'numpy.ndarray'> , element type: float64 , shape: (54321,)\n",
-      "  sample min: -7.6685, max: 19.5196\n",
-      "  sampling time: 5.48 secs\n"
+      "  sample min: -7.6696, max: 19.5197\n",
+      "  sampling time: 6.06 secs\n"
      ]
     },
     {
      "data": {
-      "image/png": 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zZQ5pQ8zMrKXa9mbViFgOLK+YdnZheDNw/CBlnDNYmda5Gj2C9RHv6DaSvy+dVNftNepe721mo8do+cftTg5aDkK2jU7+QpfB68useQ5CLTaSuwjMzFrNQagBIyFgDEUbHHjN2mMk/nYchFqgU/udR+IX2qwd/NtpHb/KwczMSuMzIbMONRyPxkdj1+xIaEOZHIRs2KvnR16rS7TdO4VadW3njmq4rItO0e4g4qD1bA5CZqOEd342HDkIlWB7bmQoc0cy2ndizXY1VT7h3BpT5lnkaPyet5uDkJVqxoy0k658CrgNb8Wd9Sc+UV492qkT74LthANHByEb0Xxkaza8OQh1sE48MrOhuXlhtH8Xhqr9jZ6pexs2zkGoScP9CzPUp+HDvf1DbbS33xozFN+XkfIddBAaQp3QH2s2VIb6EVEjQT3tGWltruQgZMDI/6JX04kHCWVup1rLvv1231hizXMQapNGdx6tvPtoNAYYa14nBmfrXG0LQpJmAp8nvYr7axHxmYr544BvAIcBDwEnRMRdkqYDC/uTAedExBU5z13AY8AzwJaImNaOttjQGy2Bs6x2jpb1W8tob/9w0pYgJGkMcCFwFLAeWCVpWUTcUkh2GvBwREyRNAc4DzgBuBmYFhFbJO0N/FbS9yNiS843IyI2tqMdZmbWWu06E5oO9EbEWgBJS4FZQDEIzQLOycOXAxdIUkT8qZBmJyCGvro2UnXKEbC7xGy0UMTQ79MlzQZmRsS78/jJwOERMa+Q5uacZn0evyOn2SjpcGARsD9wcqE77k7gYVJg+kpELKQKSXOBuQATJkw4bOnSpU214/bbm8pWt4MOqr6sffbpY5dddmlLHVqp2J6iyjaMH9/Hpk27NFxmK9dFrXJbsbxG2lfNULW5Vcuup331fhcaWW67NLv9ympDPd/lor6+rfuXgcyYMeO6objk0a4gdDzwxoogND0iPlhIsyanKQah6RHxUCHNS4BLgNdFxGZJ+0TEBkkvBH4CfDAirhmoLtOmTYvVq1c31Y6yjqI/8Ykeurq6Sq3D9qr1T5kA3d09LFnS1db6tNP2tm+gdTccDMX2G05tbrZ9ZbWh0X+A7unZun8ZiKQhCULt6o5bD+xbGJ8EbKiRZr2kscDuwKZigoi4VdLjwKHA6ojYkKc/IOkKUrffgEHIylH2jqSTjcZ1Nxrb3Cqdtu7a9WbVVcBUSQdI2hGYAyyrSLMMODUPzwaujojIecYCSNofOBi4S9LOknbN03cGjibdxGBmZh2iLWdC+c62ecBVpFu0F0XEGkkLSGc0y4CLgMWSeklnQHNy9tcCZ0h6Gvgz8IF8nehA4ApJ/e24NCKubEd7zMysNdr2f0IRsRxYXjHt7MLwZuD4KvkWA4urTF8LvLz1NTUzs3ZpV3ecmZnZNhyEzMysNH52XAfwAyLNOk+n3aVWFp8JmZlZaRyEzMysNA5CZmZWGgchMzMrjYOQmZmVxkHIzMxK4yBkZmalcRAyM7PSOAiZmVlpHITMzKw0DkJmZlYaByEzMyuNg5CZmZWmbUFI0kxJt0nqlXRGlfnjJF2W56+UNDlPny7pxvz5raS31FummZkNb20JQpLGABcCxwCHAN2SDqlIdhrwcERMAc4HzsvTbwamRcQrgJnAVySNrbNMMzMbxtp1JjQd6I2ItRHxFLAUmFWRZhZwSR6+HDhSkiLiTxGxJU/fCYgGyjQzs2GsXS+1mwisK4yvBw6vlSYitkh6FNgT2CjpcGARsD9wcp5fT5kASJoLzAWYMGECPT09TTWiu7upbNtt/Pg+urt7yll4G7h9nc3t6wy1dnt9fX1N7xNboV1BSFWmRb1pImIl8FJJLwEukfSjOssk518ILASYNm1adHV11VntZyvr7abd3T0sWdJVzsLbwO3rbG5fZ1ixovr0np4emt0ntkK7uuPWA/sWxicBG2qlkTQW2B3YVEwQEbcCjwOH1lmmmZkNY+0KQquAqZIOkLQjMAdYVpFmGXBqHp4NXB0RkfOMBZC0P3AwcFedZZqZ2TDWlu64fA1nHnAVMAZYFBFrJC0AVkfEMuAiYLGkXtIZ0Jyc/bXAGZKeBv4MfCAiNgJUK7Md7TEzs9Zo1zUhImI5sLxi2tmF4c3A8VXyLQYW11ummZl1Dj8xwczMSuMgZGZmpXEQMjOz0jgImZlZaRyEzMysNA5CZmZWGgchMzMrjYOQmZmVxkHIzMxKU3cQkrTnUFbEzMxGn0bOhNZJ+p6k2fmBoWZmZtulkSC0P/Az4KPAfZIWSnrt0FTLzMxGg7qDUEQ8GBFfiIi/AV4NPEB66vVaSQvyaxbMzMzq1uyNCS/Kn92AO0iv5r5B0hmtqpiZmY18db/KQdJLgbcDJwF9wCXAyyLinjz/k8DvgM8MQT3NzGwEauR9QtcAS4DZEfGbypkRcZekz7WsZmZmNuI1EoTeEhHXVE6UNL0/KBVfUmdmZjaYRq4J/aDG9CvrySxppqTbJPVWu3YkaZyky/L8lZIm5+lHSbpO0k357xGFPD25zBvz54UNtMfMzEo26JmQpOcASoNSHu73YmBLHWWMAS4EjgLWA6skLYuIWwrJTgMejogpkuYA5wEnABuBN0fEBkmHAleRboTod1JErB6sDmZmNvzUcya0BXgKeF4efrrwuQX4Yh1lTAd6I2JtRDwFLAVmVaSZRbrZAeBy4EhJiogbImJDnr4G2EnSuDqWaWZmw5wiYuAE6f9/BPwceF1hVgAPRsQTgy5Emg3MjIh35/GTgcMjYl4hzc05zfo8fkdOs7GinPdFxBvyeA+wJ/AM8B3g3KjSIElzgbkAEyZMOGzp0qWDVbmq229vKtt2Gz++j02bdiln4W3g9nU2t68zHHRQ9el9fX3sssvg7ZsxY8Z1ETGtxdUavDsuIu7Og9vzz6iqMq0yWAyYJt8ifh5wdGH+SRFxj6RdSUHoZOAb2xQSsRBYCDBt2rTo6upqqPL95s9vKtt26+7uYcmSrnIW3gZuX2dz+zrDihXVp/f09NDsPrEVBgxCkhZGxNw8vM3OvV9EnDLIctYD+xbGJwEbaqRZL2kssDuwKS97EnAFcEpE3FFY7j3572OSLiV1+9Wsp5mZDS+DnQndWRi+o2aqwa0Cpko6ALgHmAOcWJFmGXAqcC0wG7g6IkLSHsAPgTMj4lf9iXOg2iMiNkraATgW+Ol21NHMzNpswCAUEZ8uDDfdGRURWyTNI93ZNgZYFBFrJC0AVkfEMuAi0rPoeklnQHNy9nnAFOAsSWflaUcDjwNX5QA0hhSAvtpsHc3MrP0G6447YqD5/SLi6jrSLAeWV0w7uzC8GTi+Sr5zgXNrFHtYPfUzM7PhabDuuIvqKCOAA1tQFzMzG2UG6447oF0VMTOz0afZVzmYmZltt8GuCd0aES/Jw+vY9n97AIiI/YagbmZmNsINdk3oPYXhtw9lRczMbPQZ7JrQLwvDPx/66piZ2WhS9zUhSTtKWiDp95Iez38/KWmnoaygmZmNXI281O5LwMHAh4C7Sc+SO5P0WoV3tb5qZmY20jUShI4DXhwRj+TxWyStBHpxEDIzsyY0cov2faR3ChU9F7i3ddUxM7PRpJHH9iwGrpT0H2x94vXp+KnVZmbWpGYe2/OxivH3kt7zY2Zm1hA/tsfMzErjx/aYmVlp6r47TtJuwDnA64G9KLyO24/tMTOzZjRyJvRF4JXAAmA88EHgD8D5Q1AvMzMbBRoJQkcDb42I7wHP5L8nACfXk1nSTEm3SeqVdEaV+eMkXZbnr5Q0OU8/StJ1km7Kf48o5DksT++V9AVJqizXzMyGr0aC0HOAR/Nwn6Q9SP8jNGWwjJLGABcCxwCHAN2SDqlIdhrwcERMIZ1d9d9xtxF4c0T8FXAq6Vbxfl8C5gJT82dmA+0xM7OSNRKEfku6HgTwC1JQ+RJwex15pwO9EbE2Ip4ClgKzKtLMAi7Jw5cDR0pSRNwQERvy9DXATvmsaW9gt4i4NiKC9P9KxzXQHjMzK1kjj+15D1tvRvgQ8BlgD+CUOvJOBNYVxtcDh9dKExFbJD0K7Ek6E+r3VuCGiHhS0sRcTrHMidUWLmku6YyJCRMm0NPTU0eVt9Xd3VS27TZ+fB/d3T3lLLwN3L7O5vZ1hlq7vb6+vqb3ia1QdxCKiLWF4QdJ3Wf1qnatpvIFeQOmkfRSUhfd0Q2UmSZGLAQWAkybNi26uroGqW518+c3lW27dXf3sGRJVzkLbwO3r7O5fZ1hxYrq03t6emh2n9gKDf2fkKR3SfqJpDX572l13gzQ/5iffpOADbXSSBoL7A5syuOTgCuAUyLijkL6SYOUaWZmw1gj7xP6LPBR4LvAP+a/H6G+R/asAqZKOkDSjsAcYFlFmmWkGw8AZgNXR0TkGyB+CJwZEb/qTxwR9wKPSXpVDoSnAN+rtz1mZla+Rq4JvQN4ZUT85TqMpB8A1wP/NFDGfI1nHnAVMAZYFBFrJC0AVkfEMtJz6hZL6iWdAc3J2eeR7sA7S9JZedrREfEA8H7gYtLTvH+UP2Zm1iEaCUKP5U/ltD/WkzkilgPLK6adXRjeDBxfJd+5wLk1ylwNHFrP8s3MbPgZ7FUOBxZGPwd8V9Jn2Hr95h/xExPMzKxJg50J9ZLuOCvefDCjIs0RwAWtrJSZmY0Og73KwU/ZNjOzIdPINSEAJO1H+qfQ9RGxbrD0ZmZmtTRyi/bekn5O6qL7LnCHpGsk7TNktTMzsxGtke62L5GeH/f8iNgbeD5wA/DloaiYmZmNfI10x70W2DsingaIiMcl/RNwz5DUzMzMRrxGzoQeJr2Goehg4JHWVcfMzEaTRs6EPgv8VNJFwN3A/sA7gbMGzGVmZlZDI0/R/qqkO4ATgZeRHhbaHRFXD1XlzMxsZKsrCOU3oy4C5jromJlZq9R1TSginiG9x+fPQ1sdMzMbTRq5MeF8YL6kHYaqMmZmNro0cmPCB4EXAf9H0oNsfaZcRMR+Q1E5MzMb2RoJQm8fslqYmdmo1Eh33LXAkcDXSO8F+hrwBmDlENTLzMxGgUYf23ME8CHgb/Lf1wNfrCezpJmSbpPUK+mMKvPHSbosz18paXKevqekFZL6JF1Qkacnl3lj/rywgfaYmVnJGumOOw54cUT0PyHhFkkrSQ80fddAGfMt3hcCR5FeiLdK0rKIuKWQ7DTg4YiYImkOcB5wArCZ9A+xh1L9Laon5TesmplZh2nkTOg+4HkV054L3FtH3ulAb0SsjYingKXArIo0s4BL8vDlwJGSFBGPR8QvScHIzMxGkEbOhBYDV0r6D7a+3vt04BuSjuhPVOOfWScCxXcPrQcOr5UmIrZIehTYE9g4SL2+LukZ4DvAuRER9TfJzMzKpHr32ZLurCNZRMSBVfIeD7wxIt6dx08GpkfEBwtp1uQ06/P4HTnNQ3n8HcC0iJhXyDMxIu6RtCspCH0zIr5RZflzgbkAEyZMOGzp0qV1tbnS7bc3lW27jR/fx6ZNu5Sz8DZw+zqb29cZDjqo+vS+vj522WXw9s2YMeO6iJjW4mo19Oy4A7ZjOf1nTv0mkZ49Vy3Nekljgd2BTYPU6Z789zFJl5K6/bYJQhGxEFgIMG3atOjq6mqqEfPnN5Vtu3V397BkSVc5C28Dt6+zuX2dYcWK6tN7enpodp/YCo1cE9oeq4Cpkg6QtCMwB1hWkWYZcGoeng1cPVDXmqSxkvbKwzsAxwI3t7zmZmY2ZBq5JtS0fI1nHnAVMAZYFBFrJC0AVkfEMuAiYLGkXtIZ0Jz+/JLuAnYDdpR0HOk5dncDV+UANAb4KfDVdrTHzMxaoy1BCCAilpP+ybU47ezC8Gbg+Bp5J9co9rBW1c/MzNqvXd1xZmZm23AQMjOz0jgImZlZaRyEzMysNA5CZmZWGgchMzMrjYOQmZmVxkHIzMxK4yBkZmalcRAyM7PSOAiZmVlpHITMzKw0DkJmZlYaByEzMyuNg5CZmZXGQcjMzErjIGRmZqVpWxCSNFPSbZJ6JZ1RZf44SZfl+SslTc7T95S0QlKfpAsq8hwm6aac5wuS1J7WmJlZK7QlCEkaA1wIHAMcAnRLOqQi2WnAwxExBTgfOC9P3wycBXykStFfAuYCU/NnZutrb0zMG0YAAAn0SURBVGZmQ6VdZ0LTgd6IWBsRTwFLgVkVaWYBl+Thy4EjJSkiHo+IX5KC0V9I2hvYLSKujYgAvgEcN6StMDOzlhrbpuVMBNYVxtcDh9dKExFbJD0K7AlsHKDM9RVlTqyWUNJc0hkTEyZMoKenp8HqJ93dTWXbbuPH99Hd3VPOwtvA7etsbl9nqLXb6+vra3qf2ArtCkLVrtVEE2maSh8RC4GFANOmTYuurq4Biq1t/vymsm237u4elizpKmfhbeD2dTa3rzOsWFF9ek9PD83uE1uhXd1x64F9C+OTgA210kgaC+wObBqkzEmDlGlmZsNYu4LQKmCqpAMk7QjMAZZVpFkGnJqHZwNX52s9VUXEvcBjkl6V74o7Bfhe66tuZmZDpS3dcfkazzzgKmAMsCgi1khaAKyOiGXARcBiSb2kM6A5/fkl3QXsBuwo6Tjg6Ii4BXg/cDHwXOBH+WNmZh2iXdeEiIjlwPKKaWcXhjcDx9fIO7nG9NXAoa2rpZmZtZOfmGBmZqVxEDIzs9I4CJmZWWkchMzMrDQOQmZmVhoHITMzK42DkJmZlcZByMzMSuMgZGZmpXEQMjOz0jgImZlZaRyEzMysNA5CZmZWGgchMzMrjYOQmZmVxkHIzMxK07YgJGmmpNsk9Uo6o8r8cZIuy/NXSppcmHdmnn6bpDcWpt8l6SZJN0pa3Z6WmJlZq7TlzaqSxgAXAkcB64FVkpblV3T3Ow14OCKmSJoDnAecIOkQ0qu+XwrsA/xU0kER8UzONyMiNrajHWZm1lrtOhOaDvRGxNqIeApYCsyqSDMLuCQPXw4cKUl5+tKIeDIi7gR6c3lmZtbh2nImBEwE1hXG1wOH10oTEVskPQrsmaf/uiLvxDwcwI8lBfCViFhYbeGS5gJzASZMmEBPT09Tjejubirbdhs/vo/u7p5yFt4Gbl9nc/s6Q63dXl9fX9P7xFZoVxBSlWlRZ5qB8v7PiNgg6YXATyT9d0Rcs03iFJwWAkybNi26urrqrnjR/PlNZdtu3d09LFnSVc7C28Dt62xuX2dYsaL69J6eHprdJ7ZCu7rj1gP7FsYnARtqpZE0Ftgd2DRQ3ojo//sAcAXupjMz6yjtCkKrgKmSDpC0I+lGg2UVaZYBp+bh2cDVERF5+px899wBwFTgN5J2lrQrgKSdgaOBm9vQFjMza5G2dMflazzzgKuAMcCiiFgjaQGwOiKWARcBiyX1ks6A5uS8ayR9C7gF2AKcHhHPSJoAXJHuXWAscGlEXNmO9piZWWu065oQEbEcWF4x7ezC8Gbg+Bp5PwV8qmLaWuDlra+pmZm1i5+YYGZmpXEQMjOz0jgImZlZaRyEzMysNA5CZmZWGgchMzMrjYOQmZmVxkHIzMxK4yBkZmalcRAyM7PSOAiZmVlpHITMzKw0DkJmZlYaByEzMyuNg5CZmZXGQcjMzErjIGRmZqVpWxCSNFPSbZJ6JZ1RZf44SZfl+SslTS7MOzNPv03SG+st08zMhre2BCFJY4ALgWOAQ4BuSYdUJDsNeDgipgDnA+flvIcAc4CXAjOBL0oaU2eZZmY2jLXrTGg60BsRayPiKWApMKsizSzgkjx8OXCkJOXpSyPiyYi4E+jN5dVTppmZDWNj27ScicC6wvh64PBaaSJii6RHgT3z9F9X5J2YhwcrEwBJc4G5ebRP0m1NtKE0PT3sBWwsux5Dxe3rbG5fZ5Bqzqq3ffu3rDIF7QpC1ZofdaapNb3aWVxlmWlixEJg4UAVHM4krY6IaWXXY6i4fZ3N7etsZbevXd1x64F9C+OTgA210kgaC+wObBogbz1lmpnZMNauILQKmCrpAEk7km40WFaRZhlwah6eDVwdEZGnz8l3zx0ATAV+U2eZZmY2jLWlOy5f45kHXAWMARZFxBpJC4DVEbEMuAhYLKmXdAY0J+ddI+lbwC3AFuD0iHgGoFqZ7WhPCTq2K7FObl9nc/s6W6ntUzrZMDMzaz8/McHMzErjIGRmZqVxEOoQks6RdI+kG/PnTWXXaXuN9McuSbpL0k15e60uuz6tIGmRpAck3VyYNl7STyT9Pv99fpl1bFaNto2Y352kfSWtkHSrpDWS/iFPL3X7OQh1lvMj4hX5s7zsymyPUfTYpRl5e42U/zO5mPT4rKIzgJ9FxFTgZ3m8E13Mtm2DkfO72wJ8OCJeArwKOD3/5krdfg5CVhY/dqkDRcQ1pLtXi4qP3LoEOK6tlWqRGm0bMSLi3oi4Pg8/BtxKevpMqdvPQaizzJP0u9xt0JFdHgXVHuU0sUbaThXAjyVdlx8dNVJNiIh7Ie3ogBeWXJ9WG0m/OwDyWwr+GlhJydvPQWgYkfRTSTdX+cwCvgS8GHgFcC/wb6VWdvvV8yinTvc/I+KVpC7H0yW9ruwKWcNG2u8OSbsA3wH+d0T8sez6tOvZcVaHiHhDPekkfRX4wRBXZ6iN+McuRcSG/PcBSVeQuiCvKbdWQ+J+SXtHxL2S9gYeKLtCrRIR9/cPj4TfnaQdSAHoPyPiu3lyqdvPZ0IdIn85+r0FuLlW2g4xoh+7JGlnSbv2DwNH0/nbrJbiI7dOBb5XYl1aaiT97vKrcS4Cbo2Ify/MKnX7+YkJHULSYlKXQAB3Ae/t78ftVPl218+x9bFLnyq5Si0j6UDgijw6Frh0JLRP0hKgi/T4//uBTwD/D/gWsB/wB+D4iOi4C/w12tbFCPndSXot8AvgJuDPefLHSNeFStt+DkJmZlYad8eZmVlpHITMzKw0DkJmZlYaByEzMyuNg5CZmZXGQcisZJK6JK0vux5mZXAQMjOz0jgImZlZaRyEzFpE0hmSLq+Y9nlJX5D0zvwyscckrZX03gHKCUlTCuMXSzq3MH5sfsHaI5L+S9LLhqZFZkPPQcisdZYAb5K0G/zlxX1vAy4lPRTyWGA34J3A+ZJe2egCcp5FwHuBPYGvAMskjWtJC8zazEHIrEUi4m7gera+FOwI4E8R8euI+GFE3BHJz4EfA3/bxGLeA3wlIlZGxDMRcQnwJOlNmWYdx0HIrLUuBbrz8Il5HEnHSPq1pE2SHgHeRHpQZqP2Bz6cu+IeyWXtC+zTgrqbtZ2DkFlrfRvokjSJ9Oj/S3NX2XeAfyW9xXIPYDnVX+wH8CfgeYXxFxWG1wGfiog9Cp/nRcSSlrfErA0chMxaKCIeBHqArwN3RsStwI7AOOBBYIukY0jvF6rlRuBESWMkzQReX5j3VeB9kg5XsrOkv+t/d5FZp3EQMmu9S4E35L9ExGPAh0jvbHmY1E030Av8/gF4M/AIcBLpfT3kslaTrgtdkMvqBd7R6gaYtYvfJ2RmZqXxmZCZmZXGQcjMzErjIGRmZqVxEDIzs9I4CJmZWWkchMzMrDQOQmZmVhoHITMzK83/B+BWJ4CcNvA9AAAAAElFTkSuQmCC\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -271,8 +267,8 @@
    "execution_count": 5,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2019-08-22T01:39:21.029197Z",
-     "start_time": "2019-08-22T01:39:15.181257Z"
+     "end_time": "2019-12-12T15:17:03.611278Z",
+     "start_time": "2019-12-12T15:16:57.151934Z"
     }
    },
    "outputs": [
@@ -283,12 +279,12 @@
       "Uniform distribution over bounded integer numbers:\n",
       "  sample type: <class 'numpy.ndarray'> , element type: int64 , shape: (54321,)\n",
       "  sample min: 37, max: 841\n",
-      "  sampling time: 5.53 secs\n"
+      "  sampling time: 6.19 secs\n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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FZTaSOLnYFssbWrPWcXKxthipe1/NcsKydimua59+2aeHMZLqnFzMBuBk0T6dvrG05ji5jHDNHhF02say0+IxG651crR9F5xcDChvxS7r9NdI+qINZZprTeeWchpxODWyjo2U9fC2x257/g2vnRKnk0uHKq7U3V3dTGf68AWTdcIXrXKj2ylfJNucE2R1nfA9agcnF7MK3ijaYG0piaMRTi7WMbxRbw3P1/KNlHk6nHE6udigHLjwQLq7up8/zzuccfTb0vcUzTqJk8sI1M69kZGyh2blGU0Je0u8Rtcp31knFzMbNp2yIWyF0ZSkB6NtyUXSDOArwBjg/Ij4QkX/scC3gdcAjwHHRMTdud8c4CRgA3BaRFyeyy8EDgceiYi/KLT1r8DbgOeAO4B3RcS6lk5gHVv6imadrdM28p14a20rdNp8L1NbkoukMcC5wMFAL3CdpGURcUuh2knA4xGxt6RZwFnAMZKmArOA/YDdgJ9K2iciNgDfAs4hJaWiK4A5EbFe0lnAHOATrZvCkWk0r9jNasW8GM4diuFatqN5nRrKdcbRPF9qadeRy/5AT0TcCSBpCTATKCaXmcBncvelwDmSlMuXRMSzwF2SenJ7v4yIqyXtUTmyiPhJ4eM1wFGlTo1tkYaSLIobpkZ+sNeKGMzaSRHR+pFIRwEzIuLk/Pl44ICIOLVQ5+Zcpzd/vgM4gJRwromI/8zlFwA/iohL8+c9gMuKp8Uqxv0D4JL+4Sv6zQZmA0yYMOE1559/Pl1dXU1P322P3Va1fJ8d96lap1jeSDvjx4xnp3E71a3XyLjLNH7MeNZuWNuStosanZb+en19fQ0tx6HMl8HM3/75VdayqbUeDSamssZd1rpWK66BprmRGBr5Tg40fKet8/2GGlcj87WWAw888IaImFZZ3q4jF1Upq8xqteo0Mmz1kUpnAOuBi6r1j4gFwAKAadOmRVdXF9OnT2+k6U3UOkxeceSK6nX6mmu/u6ubo6ZvPPhq5LC85rhL1N3VzeK+xS1pu6jRaemvt3LlyprLsazTE4OZv/3zq6xlU2ynUrMxNa2wDhePoMpa12rFNdA0NxJDrXnf6DLptHW+31DjamS+NqtdyaUX2L3weRLwQI06vZK2BnYA1jY47GYknUC62H9QtOPwrMNsied4R4ot8flrncLfi/ZpV3K5DpgiaTJwP+kC/bEVdZYBJwC/JF0juSoiQtIy4GJJXyJd0J8CXDvQyPKdaZ8A3hQRfyh1SppQ5orsL0VzWr3h9fLYaLh+dzWaE+poWL/aklzyXVunApeTbkW+MCJWS5oHXB8Ry4ALgEX5gv1aUgIi11tKuvi/Hjgl3ymGpMXAdGAnSb3ApyPiAtIdZGOBK9I9AVwTEe9rx7Ra+Rr9ovXX6+7qbmU41iG2lEQzUrXtdy4RsRxYXlF2ZqH7GeDoGsPOB+ZXKa+6FYmIvYcUrJmNKKNhT3+08S/0bdTzhsfq8TpSvq2GOwAzMxt9nFzMzKx0Ti5mZlY6JxczMyudk4uZmZXOycXMzErn5GJmZqVzcjEzs9I5uZiZWemcXMzMrHROLmZmVjonFzMzK52Ti5mZlc5PRTYbBfxUX+s0PnIxM7PSObmYmVnpnFzMzKx0Ti5mZlY6JxczMytd25KLpBmS1kjqkXR6lf5jJV2S+6+StEeh35xcvkbSIYXyCyU9IunmirbGS7pC0u35/4tbOW1mZraptiQXSWOAc4FDgalAt6SpFdVOAh6PiL2Bs4Gz8rBTgVnAfsAM4LzcHsC3clml04ErI2IKcGX+bGZmbdKuI5f9gZ6IuDMingOWADMr6swEFubuS4GDJCmXL4mIZyPiLqAnt0dEXA2srTK+YlsLgbeXOTFmZjawdv2IciJwX+FzL3BArToRsV7SE8COufyaimEn1hnfhIh4MLf1oKSXVKskaTYwG2DChAn09fWxcuXKhiaoqLuru+lhmjF+zPiWj2MwHFdzOjGuTowJHFezhhrXYLZ79bQruahKWTRYp5FhByUiFgALAKZNmxZdXV1Mnz696XbmLpxbRjg1dXd1s7hvcUvHMRiOqzmdGFcnxgSOq1lDjWvFkStKjCZp12mxXmD3wudJwAO16kjaGtiBdMqrkWErPSxp19zWrsAjg47czMya1q7kch0wRdJkSduQLtAvq6izDDghdx8FXBURkctn5bvJJgNTgGvrjK/Y1gnA90uYBjMza1BbkktErAdOBS4HbgWWRsRqSfMkHZGrXQDsKKkH+DD5Dq+IWA0sBW4BfgycEhEbACQtBn4J7CupV9JJua0vAAdLuh04OH82M7M2adtTkSNiObC8ouzMQvczwNE1hp0PzK9SXvUKVkQ8Bhw0lHjNzGzw/At9MzMrnZOLmZmVzsnFzMxK5+RiZmalazi5SNqxlYGYmdno0cyRy32Svi/pqPxbFTMzs6qaSS4vIz1h+BPAQ5IWSHpDa8IyM7ORrOHkEhGPRsRXI+KvgdeRHqmySNKd+ceQL2tZlGZmNqIM9oL+LvnvRcAdpKcU/7raS8DMzGzL0/Av9CXtB7wDOA7oI70n5RURcX/u/1ngJvyoFTOzLV4zj3+5GlgMHBURmz04MiLulvTl0iIzM7MRq5nk8vf5zY+bkLR/f7IpPivMzMy2XM1cc7msRvmPywjEzMxGj7pHLpK2Ir0NUvmd9sU3Q+4FrG9RbGZmNkI1clpsPRtfK1yZSP5ElUfhm5nZlq2R5DKZdLTyM+BvC+UBPBoRT7ciMDMzG7nqJpeIuCd3+keSZmbWkAGTi6QFETE7d3+7Vr2IeGfZgZmZ2chV78jlrkL3Ha0MxMzMRo8Bk0tEfL7QPbf14ZiZ2WhQ77TYmxtpJCKuKiccMzMbDeqdFruggTYC2LNeJUkzgK8AY4DzI+ILFf3HAt8GXgM8BhwTEXfnfnOAk4ANwGkRcflAbUo6CPhX0o9E+4ATI6KngWkxM7MS1DstNrmMkUgaA5wLHAz0AtdJWhYRtxSqnQQ8HhF7S5oFnAUcI2kqMAvYD9gN+KmkffIwtdr8OjAzIm6V9E/APwMnljEtZmZW32Afud+s/YGeiLgzIp4DlgAzK+rMJD1pGeBS4KD8RICZwJKIeDYi7gJ6cnsDtRmk1wEA7AA80KLpMjOzKupdc7k1Il6eu+9j4y/1NxERL60znonAfYXPvcABtepExHpJTwA75vJrKoadmLtrtXkysFzS08CTwGurBSVpNjAbYMKECfT19bFy5co6k7K57q7upodpxvgx41s+jsFwXM3pxLg6MSZwXM0aalyD2e7VU++ay3sK3e8YwnhUpawyUdWqU6u82lFXf5sfAg6LiFWSPgZ8iZRwNq0csQBYADBt2rTo6upi+vTpVSdgIHMXtvZGuu6ubhb3LW7pOAbDcTWnE+PqxJjAcTVrqHGtOHJFidEk9a65/KLQ/bMhjKcX2L3weRKbn6rqr9MraWvS6ay1dYbdrFzSzsBfRcSqXH4JfnKzmVlbNXzNRdI2kuZJul3SU/n/ZyVt28Dg1wFTJE2WtA3pAv2yijrLgBNy91HAVRERuXyWpLGSJgNTgGsHaPNxYIfCRf+DgVsbnU4zMxu6Zl4W9nVgX+A04B7Ss8bmkK5/vHugAfM1lFOBy0m3DV8YEaslzQOuj4hlpNueF0nqIR2xzMrDrpa0FLiF9FTmUyJiA0C1NnP5e4DvSPoTKdkMGJ+ZmZWrmeTydmCviFiXP98iaRXp7q26G++IWA4sryg7s9D9DHB0jWHnU+XR/tXazOXfA75XLyYzM2uNZm5FfgjYrqLshcCD5YVjZmajQTOPf1kE/FjS19h4kf0U0q/qzczMnjeYx798suLze0m/pjczMwPa9PgXMzPbsrTr8S9mZrYFafhuMUkvAj4DvAnYicIv5xt4/IuZmW1BmjlyOQ94NTAPGA/8P+Be4OwWxGVmZiNYM79zeSvw8oh4TNKGiPi+pOuBH+AEY2ZmBc0cuWwFPJG7+ySNI/3GZe/SozIzsxGtmSOXG0nXW64Efk56UVcfcFsL4jIzsxGsmSOX9wB35+7TgGeAccA7S47JzMxGuIaPXCLizkL3o6TXEpuZmW2mqd+5SHq3pCskrc7/T8qvIjYzM3teM79z+SLpHfVfZuMj9z9Kegz/x1sSnZmZjUjNXNA/EXh1RPT2F0i6DPgVTi5mZlbQzGmx3+e/yrInywvHzMxGg3qP3N+z8PHLwHclfYGNj9z/GP4BpZmZVah3WqwHCArPEQMOrKjzZuCcMoMyM7ORrd4j9/3UZDMza1ozF/QBkPRSYCLQGxH3lR+SmZmNdA0fmUjaVdLPSKfKvgvcIelqSbs1OPwMSWsk9Ug6vUr/sZIuyf1XSdqj0G9OLl8j6ZB6bSqZL+k2SbdKOq3R6TQzs6Fr5rTX10nPF3txROwKvBj4NfCNegNKGkN6FtmhwFSgW9LUimonAY9HxN6kmwTOysNOBWYB+wEzgPMkjanT5omkGw7+PCJeDixpYjrNzGyImjkt9gZg14j4I0BEPCXp48D9DQy7P9DT/wgZSUtIP8i8pVBnJullZACXAufkX//PBJZExLPAXZJ6cnsM0Ob7gWMj4k851keamE4zMxuiZpLL46QjhBsLZfsC6xoYdiJQvD7TCxxQq05ErJf0BLBjLr+mYtiJubtWm3sBx0j6e+BR4LSIuL0yKEmzgdkAEyZMoK+vj5UrVzYwOZvq7upuephmjB8zvuXjGAzH1ZxOjKsTYwLH1ayhxjWY7V49zSSXLwI/lXQBGx//8i7gUw0MW+35Y9FgnVrl1U7p9bc5FngmIqZJ+gfgQuCNm1WOWAAsAJg2bVp0dXUxffr0qhMwkLkL5zY9TDO6u7pZ3Le4peMYDMfVnE6MqxNjAsfVrKHGteLIFSVGkzR8zSUi/gM4BtgJeFv+35030PX0/+iy3yTggVp1JG0N7ACsHWDYgdrsBb6Tu78HvKKBGM3MrCQNJZd8AX0h8D8RcXJEHJb/X9XgeK4DpkiaLGkb0gX6ZRV1lgEn5O6jgKsiInL5rHw32WRgCnBtnTb/m/TjTkgvOPMLzczM2qih02IRsUHSW4E/DWYk+RrKqcDlwBjgwohYLWkecH1ELAMuABblC/ZrScmCXG8p6UL9euCUiNgAUK3NPMovABdJ+hDpbZknDyZuMzMbnGauuZwNzJX06f47xpoREcuB5RVlZxa6nwGOrjHsfGB+I23m8nXA3zUbo5mZlaOZ5PL/gF2AD0t6lI0X2yMiXtqK4MzMbGRqJrm8o2VRmJnZqNLML/R/CRwEnE86FXU+8BZgVQviMjOzEayZI5evk340eRobf+cyh/SDxneXH5qZmY1UzSSXtwN75YvlALdIWkV6kKWTi5mZPa+Z02IPAdtVlL0QeLC8cMzMbDRo5shlEfBjSV9j46/jTwG+Lan/B4s08cNKMzMbpZpJLu/N/z9ZUf6+/Afp9uQ9hxqUmZmNbA0nl4iY3MpAzMxs9GjmmouZmVlDnFzMzKx0Ti5mZlY6JxczMyudk4uZmZXOycXMzErn5GJmZqVzcjEzs9I5uZiZWemcXMzMrHROLmZmVjonFzMzK13bkoukGZLWSOqRdHqV/mMlXZL7r5K0R6HfnFy+RtIhTbT5NUl9rZomMzOrri3JRdIY4FzgUGAq0C1pakW1k4DHI2Jv4GzgrDzsVGAWsB8wAzhP0ph6bUqaBoxr6YSZmVlV7Tpy2R/oiYg7I+I5YAkws6LOTGBh7r4UOEiScvmSiHg2Iu4ivVZ5/4HazInnX4GPt3i6zMysimZeFjYUE4H7Cp97gQNq1YmI9ZKeAHbM5ddUDDsxd9dq81RgWUQ8mPJTdZJmA7MBJkyYQF9fHytXrmx8qrLuru6mh2nG+DHjWz6OwXBczenEuDoxJnBczRpqXIPZ7tXTruRSbQsfDdapVV7tqCsk7QYcDUyvF1RELAAWAEybNi26urqYPr3uYJuZu3Bu08M0o7urm8V9i1s6jsFwXM3pxLg6MSZwXM0aalwrjlxRYjRJu06L9QK7Fz5PAh6oVUfS1sAOwNoBhq1V/ipgb6BH0t3AdpJ6ypoQMzOrr13J5TpgiqTJkrYhXaBfVlFnGXBC7j4KuCoiIpfPyneTTS8LLXwAAAuRSURBVAamANfWajMifhgRu0TEHhGxB/CHfJOAmZm1SVtOi+VrKKcClwNjgAsjYrWkecD1EbEMuABYlI8y1pKSBbneUuAWYD1wSkRsAKjWZjumx8zMBtauay5ExHJgeUXZmYXuZ0jXSqoNOx+Y30ibVep0DSZeMzMbPP9C38zMSufkYmZmpXNyMTOz0jm5mJlZ6ZxczMysdE4uZmZWOicXMzMrnZOLmZmVzsnFzMxK5+RiZmalc3IxM7PSObmYmVnpnFzMzKx0Ti5mZlY6JxczMyudk4uZmZXOycXMzErn5GJmZqVzcjEzs9I5uZiZWenallwkzZC0RlKPpNOr9B8r6ZLcf5WkPQr95uTyNZIOqdempIty+c2SLpT0glZPn5mZbdSW5CJpDHAucCgwFeiWNLWi2knA4xGxN3A2cFYediowC9gPmAGcJ2lMnTYvAv4c+EvghcDJLZw8MzOr0K4jl/2Bnoi4MyKeA5YAMyvqzAQW5u5LgYMkKZcviYhnI+IuoCe3V7PNiFgeGXAtMKnF02dmZgVbt2k8E4H7Cp97gQNq1YmI9ZKeAHbM5ddUDDsxdw/YZj4ddjzwgWpBSZoNzAaYMGECfX19rFy5suGJ6tfd1d30MM0YP2Z8y8cxGI6rOZ0YVyfGBI6rWUONazDbvXralVxUpSwarFOrvNpRV2Wb5wFXR8TPqwUVEQuABQDTpk2Lrq4upk+fXq3qgOYunNv0MM3o7upmcd/ilo5jMBxXczoxrk6MCRxXs4Ya14ojV5QYTdKu5NIL7F74PAl4oEadXklbAzsAa+sMW7NNSZ8GdgbeW0L8ZmbWhHZdc7kOmCJpsqRtSBfol1XUWQackLuPAq7K10yWAbPy3WSTgSmk6yg125R0MnAI0B0Rf2rxtJmZWYW2HLnkayinApcDY4ALI2K1pHnA9RGxDLgAWCSph3TEMisPu1rSUuAWYD1wSkRsAKjWZh7lN4B7gF+mewL4bkTMa8e0mplZ+06LERHLgeUVZWcWup8Bjq4x7HxgfiNt5vK2TZeZmW3Ov9A3M7PSObmYmVnpnFzMzKx0Ti5mZlY6JxczMyudk4uZmZXOycXMzErn5GJmZqVzcjEzs9I5uZiZWemcXMzMrHROLmZmVjonFzMzK52Ti5mZlc7JxczMSufkYmZmpXNyMTOz0jm5mJlZ6ZxczMysdE4uZmZWOicXMzMrXduSi6QZktZI6pF0epX+YyVdkvuvkrRHod+cXL5G0iH12pQ0Obdxe25zm1ZPn5mZbdSW5CJpDHAucCgwFeiWNLWi2knA4xGxN3A2cFYediowC9gPmAGcJ2lMnTbPAs6OiCnA47ltMzNrk3YduewP9ETEnRHxHLAEmFlRZyawMHdfChwkSbl8SUQ8GxF3AT25vapt5mHenNsgt/n2Fk6bmZlV2LpN45kI3Ff43AscUKtORKyX9ASwYy6/pmLYibm7Wps7AusiYn2V+puQNBuYnT/2HXjggY8Bv2t8stpjJSt3wnE1zHE1rhNjAsfVrKHGpRM1lNG/rFphu5JLtcijwTq1yqsddQ1Uf/PCiAXAgucDkK6PiGnV6g4nx9Ucx9W4TowJHFezOjGudp0W6wV2L3yeBDxQq46krYEdgLUDDFur/HfAuNxGrXGZmVkLtSu5XAdMyXdxbUO6QL+sos4y4ITcfRRwVURELp+V7yabDEwBrq3VZh5mRW6D3Ob3WzhtZmZWoS2nxfI1lFOBy4ExwIURsVrSPOD6iFgGXAAsktRDOmKZlYddLWkpcAuwHjglIjYAVGszj/ITwBJJnwN+ndtuxIL6VYaF42qO42pcJ8YEjqtZHReX0o6+mZlZefwLfTMzK52Ti5mZlc7JJav3eJoWj/tCSY9IurlQNl7SFfkRNldIenEul6Sv5jhvkvTqFsW0u6QVkm6VtFrSBzokrm0lXSvpxhzX3Fxe9ZE/Az1WqEXxjZH0a0mXdUpcku6W9FtJv5F0fS4b1uWYxzVO0qWS/i+vZ68b7rgk7ZvnU//fk5I+2AFxfSiv7zdLWpy/B8O+bg0oIrb4P9INAXcAewLbADcCU9s4/r8FXg3cXCj7InB67j4dOCt3Hwb8iPR7ntcCq1oU067Aq3P3nwG3kR6zM9xxCejK3S8AVuXxLQVm5fJvAO/P3f8EfCN3zwIuafGy/DBwMXBZ/jzscQF3AztVlA3rcszjWgicnLu3AcZ1QlyF+MYAD5F+JDhscZF+BH4X8MLCOnViJ6xbA8Y9HCPttD/gdcDlhc9zgDltjmEPNk0ua4Bdc/euwJrc/e9Ad7V6LY7v+8DBnRQXsB3wK9KTGX4HbF25PEl3E74ud2+d66lF8UwCriQ9fuiyvMHphLjuZvPkMqzLEXhR3mCqk+KqiOWtwP8Md1xsfHrJ+LyuXAYc0gnr1kB/Pi2WVHs8TdVHxrTRhIh4ECD/f0kub3us+bD6VaSjhGGPK596+g3wCHAF6aiz1iN/NnmsEND/WKFW+DLwceBP+fNAjyJqZ1wB/ETSDUqPPILhX457Ao8C38ynEc+XtH0HxFU0C1icu4ctroi4H/g34F7gQdK6cgOdsW7V5OSSNPzImA7Q1lgldQHfAT4YEU8OVLVKWUviiogNEfFK0pHC/sDLBxh3W+KSdDjwSETcUCwe7riyv4mIV5OeIH6KpL8doG674tqadCr46xHxKuAp0umm4Y4rjSxdvzgC+K96VauUlRpXvr4zE5gM7AZsT1qWtcbbEdszJ5ekkcfTtNvDknYFyP8fyeVti1XSC0iJ5aKI+G6nxNUvItYBK0nnums98qfWY4XK9jfAEZLuJj2h+82kI5nhjouIeCD/fwT4HikhD/dy7AV6I2JV/nwpKdkMd1z9DgV+FREP58/DGddbgLsi4tGI+CPwXeD1dMC6NRAnl6SRx9O0W/FxOMVH2CwD3pnvUnkt8ET/4XqZJIn0ZINbI+JLHRTXzpLG5e4Xkr54t1L7kT+1HitUqoiYExGTImIP0vpzVUQcN9xxSdpe0p/1d5OuI9zMMC/HiHgIuE/SvrnoINJTOIY1roJuNp4S6x//cMV1L/BaSdvl72X/vBrWdauudl/k6dQ/0l0ft5HO35/R5nEvJp1L/SNpr+Mk0jnSK4Hb8//xua5IL0m7A/gtMK1FMb2BdCh9E/Cb/HdYB8T1CtIjfW4ibSTPzOV7kp4510M6lTE2l2+bP/fk/nu2YXlOZ+PdYsMaVx7/jflvdf+6PdzLMY/rlcD1eVn+N/DiDolrO+AxYIdC2XCv93OB/8vr/CJg7HCvW/X+/PgXMzMrnU+LmZlZ6ZxczMysdE4uZmZWOicXMzMrnZOLmZmVzsnFrENJmi6pd7jjMBsMJxczMyudk4uZmZXOycWsxSSdLunSirKv5JdMvUvpRVm/l3SnpPcO0E5I2rvw+VuSPlf4fHh+wdU6Sf8r6RWtmSKz+pxczFpvMXCYpBdBemUA8I+kl4o9AhxOer/Ju4CzB/M2wzzMhcB7SY8q+XdgmaSxpUyBWZOcXMxaLCLuIb3U7O256M3AHyLimoj4YUTcEcnPgJ8AbxzEaN4D/HtErIr0SoKFwLOkJ0abtZ2Ti1l7XEx60i7Asfkzkg6VdI2ktZLWkR4OutMg2n8Z8JF8Smxdbmt30vs/zNrOycWsPf4LmC5pEvD3wMX5lNV3SG8ZnBAR44DlVH/ZE8AfSE/s7bdLofs+YH5EjCv8bRcRizEbBk4uZm0QEY+SXmz2TdKLn24FtiE9Ov1RYL2kQ0nvW6nlN8Cx+TXPM4A3Ffr9B/A+SQfkd4tsL+nv+t/lYtZuTi5m7XMx6eVmFwNExO+B04ClwOOk02UDvaTuA8DbgHXAcaR3oJDbup503eWc3FYPcGLZE2DWKL/PxczMSucjFzMzK52Ti5mZlc7JxczMSufkYmZmpXNyMTOz0jm5mJlZ6ZxczMysdE4uZmZWuv8PrJ5Die9qVtQAAAAASUVORK5CYII=\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -352,8 +348,8 @@
    "execution_count": 6,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2019-08-22T01:39:21.059196Z",
-     "start_time": "2019-08-22T01:39:21.040829Z"
+     "end_time": "2019-12-12T15:17:03.733239Z",
+     "start_time": "2019-12-12T15:17:03.721731Z"
     }
    },
    "outputs": [],
@@ -409,8 +405,8 @@
    "execution_count": 7,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2019-08-22T01:39:24.088003Z",
-     "start_time": "2019-08-22T01:39:22.505982Z"
+     "end_time": "2019-12-12T15:17:05.431487Z",
+     "start_time": "2019-12-12T15:17:03.757483Z"
     }
    },
    "outputs": [
@@ -420,13 +416,13 @@
      "text": [
       "Normal distribution (mu=2.400, sigma=5.100):\n",
       "  sample type: <class 'numpy.ndarray'> , element type: float64 , shape: (4321,)\n",
-      "  sample min: -16.1580, max: 20.3295\n",
-      "  sampling time: 1.32 secs\n"
+      "  sample min: -14.8045, max: 20.6493\n",
+      "  sampling time: 1.45 secs\n"
      ]
     },
     {
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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -477,19 +473,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 8,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2019-08-22T01:39:36.760562Z",
-     "start_time": "2019-08-22T01:39:36.752562Z"
+     "end_time": "2019-12-12T15:17:05.541680Z",
+     "start_time": "2019-12-12T15:17:05.485957Z"
     }
    },
    "outputs": [
     {
      "data": {
       "text/html": [
-       "<h3>Version Information</h3><table><tr><th>Qiskit Software</th><th>Version</th></tr><tr><td>Qiskit</td><td>None</td></tr><tr><td>Terra</td><td>0.9.0</td></tr><tr><td>Aer</td><td>0.3.0</td></tr><tr><td>Ignis</td><td>0.2.0</td></tr><tr><td>Aqua</td><td>0.5.6</td></tr><tr><td>IBM Q Provider</td><td>0.3.2rc1</td></tr><tr><th>System information</th></tr><tr><td>Python</td><td>3.7.4 (default, Aug 13 2019, 15:17:50) \n",
-       "[Clang 4.0.1 (tags/RELEASE_401/final)]</td></tr><tr><td>OS</td><td>Darwin</td></tr><tr><td>CPUs</td><td>4</td></tr><tr><td>Memory (Gb)</td><td>16.0</td></tr><tr><td colspan='2'>Wed Aug 21 21:39:36 2019 EDT</td></tr></table>"
+       "<h3>Version Information</h3><table><tr><th>Qiskit Software</th><th>Version</th></tr><tr><td>Qiskit</td><td>0.14.0</td></tr><tr><td>Terra</td><td>0.11.0</td></tr><tr><td>Aer</td><td>0.3.4</td></tr><tr><td>Ignis</td><td>0.2.0</td></tr><tr><td>Aqua</td><td>0.6.1</td></tr><tr><td>IBM Q Provider</td><td>0.4.4</td></tr><tr><th>System information</th></tr><tr><td>Python</td><td>3.7.5 (default, Oct 25 2019, 10:52:18) \n",
+       "[Clang 4.0.1 (tags/RELEASE_401/final)]</td></tr><tr><td>OS</td><td>Darwin</td></tr><tr><td>CPUs</td><td>4</td></tr><tr><td>Memory (Gb)</td><td>16.0</td></tr><tr><td colspan='2'>Thu Dec 12 10:17:05 2019 EST</td></tr></table>"
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -541,7 +537,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.4"
+   "version": "3.7.5"
   },
   "varInspector": {
    "cols": {
diff --git a/qiskit/advanced/aqua/linear_systems_of_equations.ipynb b/qiskit/advanced/aqua/linear_systems_of_equations.ipynb
index 8cc5fdddf..d22543be4 100644
--- a/qiskit/advanced/aqua/linear_systems_of_equations.ipynb
+++ b/qiskit/advanced/aqua/linear_systems_of_equations.ipynb
@@ -1,23 +1,22 @@
 {
  "cells": [
   {
+   "attachments": {
+    "qiskit_header.png": {
+     "image/png": 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"
+    }
+   },
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<img src=\"../../../images/qiskit_header.png\" alt=\"Note: In order for images to show up in this jupyter notebook you need to select File => Trusted Notebook\" align=\"middle\">"
+    "![qiskit_header.png](attachment:qiskit_header.png)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# _*Qiskit Aqua: Solving linear systems of equations with the HHL algorithm*_\n",
-    "\n",
-    "***\n",
-    "### Contributors\n",
-    "David Bucher<sup>[1]</sup>, Jan Mueggenburg<sup>[1]</sup>, Gawel Kus<sup>[1]</sup>, Isabel Haide<sup>[1]</sup>, Shubha Deutschle<sup>[1]</sup>, Harry Barowski<sup>[1]</sup>, Dominik Steenken<sup>[1]</sup>, and Albert Frisch<sup>[1]</sup>\n",
-    "### Affiliation\n",
-    "- <sup>[1]</sup>IBMQ"
+    "# Qiskit Aqua: Solving linear systems of equations with the HHL algorithm"
    ]
   },
   {
@@ -46,8 +45,8 @@
    "execution_count": 1,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2019-08-22T01:39:51.130989Z",
-     "start_time": "2019-08-22T01:39:48.567368Z"
+     "end_time": "2019-12-12T15:17:36.454540Z",
+     "start_time": "2019-12-12T15:17:34.260773Z"
     }
    },
    "outputs": [],
@@ -64,8 +63,8 @@
    "execution_count": 2,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2019-08-22T01:39:51.149609Z",
-     "start_time": "2019-08-22T01:39:51.144479Z"
+     "end_time": "2019-12-12T15:17:36.473490Z",
+     "start_time": "2019-12-12T15:17:36.467720Z"
     }
    },
    "outputs": [],
@@ -126,8 +125,8 @@
    "execution_count": 3,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2019-08-22T01:39:51.435207Z",
-     "start_time": "2019-08-22T01:39:51.431997Z"
+     "end_time": "2019-12-12T15:17:37.063044Z",
+     "start_time": "2019-12-12T15:17:37.059521Z"
     }
    },
    "outputs": [],
@@ -146,8 +145,8 @@
    "execution_count": 4,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2019-08-22T01:40:22.904918Z",
-     "start_time": "2019-08-22T01:39:51.718509Z"
+     "end_time": "2019-12-12T15:18:02.001641Z",
+     "start_time": "2019-12-12T15:17:37.409036Z"
     }
    },
    "outputs": [
@@ -155,8 +154,8 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Numpy 1.16 has memory leak bug  https://github.com/numpy/numpy/issues/13808\n",
-      "It is recommended to downgrade to numpy 1.15 or older\n"
+      "/Users/paul/opt/anaconda3/envs/qiskit/lib/python3.7/site-packages/qiskit/providers/models/backendconfiguration.py:337: UserWarning: `dt` and `dtm` now have units of seconds(s) rather than nanoseconds(ns).\n",
+      "  warnings.warn('`dt` and `dtm` now have units of seconds(s) rather '\n"
      ]
     },
     {
@@ -193,8 +192,8 @@
    "execution_count": 5,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2019-08-22T01:40:50.874643Z",
-     "start_time": "2019-08-22T01:40:22.925404Z"
+     "end_time": "2019-12-12T15:18:19.162073Z",
+     "start_time": "2019-12-12T15:18:02.019372Z"
     }
    },
    "outputs": [
@@ -237,8 +236,8 @@
    "execution_count": 6,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2019-08-22T01:40:50.892442Z",
-     "start_time": "2019-08-22T01:40:50.888521Z"
+     "end_time": "2019-12-12T15:18:19.180442Z",
+     "start_time": "2019-12-12T15:18:19.176798Z"
     }
    },
    "outputs": [
@@ -247,7 +246,7 @@
      "output_type": "stream",
      "text": [
       "circuit_width 7\n",
-      "circuit_depth 129\n"
+      "circuit_depth 28820\n"
      ]
     }
    ],
@@ -281,8 +280,8 @@
    "execution_count": 7,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2019-08-22T01:40:50.912216Z",
-     "start_time": "2019-08-22T01:40:50.908085Z"
+     "end_time": "2019-12-12T15:18:19.201100Z",
+     "start_time": "2019-12-12T15:18:19.197397Z"
     }
    },
    "outputs": [],
@@ -305,13 +304,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "metadata": {
     "ExecuteTime": {
-     "start_time": "2019-08-22T01:40:04.769Z"
+     "end_time": "2019-12-12T15:32:01.767831Z",
+     "start_time": "2019-12-12T15:18:19.214202Z"
     }
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "solution  [0.14218+0.j 0.28625-0.j]\n",
+      "classical solution  [0.14286 0.28571]\n",
+      "probability 0.000316\n",
+      "fidelity 0.999993\n"
+     ]
+    }
+   ],
    "source": [
     "result = run_algorithm(params3)\n",
     "print(\"solution \", np.round(result['solution'], 5))\n",
@@ -332,13 +343,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "metadata": {
     "ExecuteTime": {
-     "start_time": "2019-08-22T01:40:07.544Z"
+     "end_time": "2019-12-12T15:32:01.790135Z",
+     "start_time": "2019-12-12T15:32:01.786074Z"
     }
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "circuit_width 11\n",
+      "circuit_depth 1284811\n"
+     ]
+    }
+   ],
    "source": [
     "print(\"circuit_width\", result['circuit_info']['width'])\n",
     "print(\"circuit_depth\", result['circuit_info']['depth'])"
@@ -372,10 +393,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "metadata": {
     "ExecuteTime": {
-     "start_time": "2019-08-22T01:40:08.096Z"
+     "end_time": "2019-12-12T15:32:01.814561Z",
+     "start_time": "2019-12-12T15:32:01.806695Z"
     }
    },
    "outputs": [],
@@ -402,7 +424,7 @@
    "execution_count": null,
    "metadata": {
     "ExecuteTime": {
-     "start_time": "2019-08-22T01:40:08.599Z"
+     "start_time": "2019-12-12T15:17:43.553Z"
     }
    },
    "outputs": [],
@@ -429,7 +451,7 @@
    "execution_count": null,
    "metadata": {
     "ExecuteTime": {
-     "start_time": "2019-08-22T01:40:09.363Z"
+     "start_time": "2019-12-12T15:17:44.321Z"
     }
    },
    "outputs": [],
@@ -457,7 +479,7 @@
    "execution_count": null,
    "metadata": {
     "ExecuteTime": {
-     "start_time": "2019-08-22T01:40:10.519Z"
+     "start_time": "2019-12-12T15:17:45.437Z"
     }
    },
    "outputs": [],
@@ -480,7 +502,7 @@
    "execution_count": null,
    "metadata": {
     "ExecuteTime": {
-     "start_time": "2019-08-22T01:40:11.498Z"
+     "start_time": "2019-12-12T15:17:46.308Z"
     }
    },
    "outputs": [],
@@ -525,7 +547,7 @@
    "execution_count": null,
    "metadata": {
     "ExecuteTime": {
-     "start_time": "2019-08-22T01:40:12.367Z"
+     "start_time": "2019-12-12T15:17:47.072Z"
     }
    },
    "outputs": [],
@@ -565,7 +587,7 @@
    "execution_count": null,
    "metadata": {
     "ExecuteTime": {
-     "start_time": "2019-08-22T01:40:13.243Z"
+     "start_time": "2019-12-12T15:17:47.852Z"
     }
    },
    "outputs": [],
@@ -576,19 +598,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2019-08-22T01:53:14.511056Z",
-     "start_time": "2019-08-22T01:53:14.501261Z"
+     "end_time": "2019-12-12T17:24:48.341293Z",
+     "start_time": "2019-12-12T17:24:45.000274Z"
     }
    },
    "outputs": [
     {
      "data": {
       "text/html": [
-       "<h3>Version Information</h3><table><tr><th>Qiskit Software</th><th>Version</th></tr><tr><td>Qiskit</td><td>None</td></tr><tr><td>Terra</td><td>0.9.0</td></tr><tr><td>Aer</td><td>0.3.0</td></tr><tr><td>Ignis</td><td>0.2.0</td></tr><tr><td>Aqua</td><td>0.5.6</td></tr><tr><td>IBM Q Provider</td><td>0.3.2rc1</td></tr><tr><th>System information</th></tr><tr><td>Python</td><td>3.7.4 (default, Aug 13 2019, 15:17:50) \n",
-       "[Clang 4.0.1 (tags/RELEASE_401/final)]</td></tr><tr><td>OS</td><td>Darwin</td></tr><tr><td>CPUs</td><td>4</td></tr><tr><td>Memory (Gb)</td><td>16.0</td></tr><tr><td colspan='2'>Wed Aug 21 21:53:14 2019 EDT</td></tr></table>"
+       "<h3>Version Information</h3><table><tr><th>Qiskit Software</th><th>Version</th></tr><tr><td>Qiskit</td><td>0.14.0</td></tr><tr><td>Terra</td><td>0.11.0</td></tr><tr><td>Aer</td><td>0.3.4</td></tr><tr><td>Ignis</td><td>0.2.0</td></tr><tr><td>Aqua</td><td>0.6.1</td></tr><tr><td>IBM Q Provider</td><td>0.4.4</td></tr><tr><th>System information</th></tr><tr><td>Python</td><td>3.7.5 (default, Oct 25 2019, 10:52:18) \n",
+       "[Clang 4.0.1 (tags/RELEASE_401/final)]</td></tr><tr><td>OS</td><td>Darwin</td></tr><tr><td>CPUs</td><td>4</td></tr><tr><td>Memory (Gb)</td><td>16.0</td></tr><tr><td colspan='2'>Thu Dec 12 12:24:48 2019 EST</td></tr></table>"
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -640,7 +662,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.4"
+   "version": "3.7.5"
   },
   "varInspector": {
    "cols": {
diff --git a/qiskit/advanced/aqua/optimization/docplex.ipynb b/qiskit/advanced/aqua/optimization/docplex.ipynb
index 983d401b8..bc8244ad8 100644
--- a/qiskit/advanced/aqua/optimization/docplex.ipynb
+++ b/qiskit/advanced/aqua/optimization/docplex.ipynb
@@ -1,25 +1,22 @@
 {
  "cells": [
   {
+   "attachments": {
+    "qiskit_header.png": {
+     "image/png": 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"
+    }
+   },
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<img src=\"../../../../images/qiskit_header.png\" alt=\"Note: In order for images to show up in this jupyter notebook you need to select File => Trusted Notebook\" align=\"middle\">"
+    "![qiskit_header.png](attachment:qiskit_header.png)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# _*Qiskit Aqua: Generating Ising Hamiltonians from optimization models with DOcplex*_\n",
-    "\n",
-    "The latest version of this notebook is available on https://github.com/Qiskit/qiskit-tutorial.\n",
-    "\n",
-    "***\n",
-    "### Contributors\n",
-    "Atsushi Matsuo<sup>[1]</sup>, Takashi Imamichi<sup>[1]</sup>, Marco Pistoia<sup>[1]</sup>, Stephen Wood<sup>[1]</sup>\n",
-    "### Affiliation\n",
-    "- <sup>[1]</sup>IBMQ"
+    "# Qiskit Aqua: Generating Ising Hamiltonians from optimization models with DOcplex"
    ]
   },
   {
@@ -72,12 +69,17 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:58:30.165012Z",
+     "start_time": "2019-12-12T17:58:27.219278Z"
+    }
+   },
    "outputs": [],
    "source": [
     "from docplex.mp.model import Model\n",
     "\n",
-    "from qiskit import BasicAer\n",
+    "from qiskit import Aer\n",
     "from qiskit.aqua import run_algorithm\n",
     "from qiskit.aqua.algorithms import VQE, ExactEigensolver\n",
     "from qiskit.aqua.components.optimizers import SPSA\n",
@@ -106,6 +108,10 @@
    "cell_type": "code",
    "execution_count": 2,
    "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:58:30.226990Z",
+     "start_time": "2019-12-12T17:58:30.188320Z"
+    },
     "scrolled": true
    },
    "outputs": [
@@ -123,10 +129,10 @@
       " c1: x_1 + 2 x_2 + 3 x_3 + 4 x_4 = 3\n",
       "\n",
       "Bounds\n",
-      "0 <= x_1 <= 1\n",
-      "0 <= x_2 <= 1\n",
-      "0 <= x_3 <= 1\n",
-      "0 <= x_4 <= 1\n",
+      " 0 <= x_1 <= 1\n",
+      " 0 <= x_2 <= 1\n",
+      " 0 <= x_3 <= 1\n",
+      " 0 <= x_4 <= 1\n",
       "\n",
       "Binaries\n",
       " x_1 x_2 x_3 x_4\n",
@@ -160,7 +166,12 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:58:31.906551Z",
+     "start_time": "2019-12-12T17:58:31.896319Z"
+    }
+   },
    "outputs": [],
    "source": [
     "qubitOp, offset = docplex.get_qubitops(mdl)"
@@ -176,7 +187,12 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:58:33.912049Z",
+     "start_time": "2019-12-12T17:58:33.883348Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -210,15 +226,20 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:59:00.458990Z",
+     "start_time": "2019-12-12T17:58:38.091633Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "energy: -56.94978190681336\n",
-      "time: 60.2764310836792\n",
-      "solution objective: -1.4497819068133566\n",
+      "energy: -56.74704235348765\n",
+      "time: 22.31848978996277\n",
+      "solution objective: -1.2470423534876502\n",
       "solution: [1. 1. 0. 0.]\n"
      ]
     }
@@ -230,7 +251,7 @@
     "ry = RY(qubitOp.num_qubits, depth=5, entanglement='linear')\n",
     "vqe = VQE(qubitOp, ry, spsa)\n",
     "\n",
-    "backend = BasicAer.get_backend('statevector_simulator')\n",
+    "backend = Aer.get_backend('statevector_simulator')\n",
     "quantum_instance = QuantumInstance(backend, seed_simulator=seed, seed_transpiler=seed)\n",
     "\n",
     "result = vqe.run(quantum_instance)\n",
@@ -271,19 +292,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 6,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2019-08-22T01:57:59.094207Z",
-     "start_time": "2019-08-22T01:57:59.085241Z"
+     "end_time": "2019-12-12T17:59:00.620136Z",
+     "start_time": "2019-12-12T17:59:00.485127Z"
     }
    },
    "outputs": [
     {
      "data": {
       "text/html": [
-       "<h3>Version Information</h3><table><tr><th>Qiskit Software</th><th>Version</th></tr><tr><td>Qiskit</td><td>None</td></tr><tr><td>Terra</td><td>0.9.0</td></tr><tr><td>Aer</td><td>0.3.0</td></tr><tr><td>Ignis</td><td>0.2.0</td></tr><tr><td>Aqua</td><td>0.5.6</td></tr><tr><td>IBM Q Provider</td><td>0.3.2rc1</td></tr><tr><th>System information</th></tr><tr><td>Python</td><td>3.7.4 (default, Aug 13 2019, 15:17:50) \n",
-       "[Clang 4.0.1 (tags/RELEASE_401/final)]</td></tr><tr><td>OS</td><td>Darwin</td></tr><tr><td>CPUs</td><td>4</td></tr><tr><td>Memory (Gb)</td><td>16.0</td></tr><tr><td colspan='2'>Wed Aug 21 21:57:59 2019 EDT</td></tr></table>"
+       "<h3>Version Information</h3><table><tr><th>Qiskit Software</th><th>Version</th></tr><tr><td>Qiskit</td><td>0.14.0</td></tr><tr><td>Terra</td><td>0.11.0</td></tr><tr><td>Aer</td><td>0.3.4</td></tr><tr><td>Ignis</td><td>0.2.0</td></tr><tr><td>Aqua</td><td>0.6.1</td></tr><tr><td>IBM Q Provider</td><td>0.4.4</td></tr><tr><th>System information</th></tr><tr><td>Python</td><td>3.7.5 (default, Oct 25 2019, 10:52:18) \n",
+       "[Clang 4.0.1 (tags/RELEASE_401/final)]</td></tr><tr><td>OS</td><td>Darwin</td></tr><tr><td>CPUs</td><td>4</td></tr><tr><td>Memory (Gb)</td><td>16.0</td></tr><tr><td colspan='2'>Thu Dec 12 12:59:00 2019 EST</td></tr></table>"
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -335,7 +356,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.4"
+   "version": "3.7.5"
   },
   "varInspector": {
    "cols": {
diff --git a/qiskit/advanced/aqua/optimization/index.ipynb b/qiskit/advanced/aqua/optimization/index.ipynb
index 74fe63b58..a5ee42b5f 100644
--- a/qiskit/advanced/aqua/optimization/index.ipynb
+++ b/qiskit/advanced/aqua/optimization/index.ipynb
@@ -1,10 +1,15 @@
 {
  "cells": [
   {
+   "attachments": {
+    "qiskit_header.png": {
+     "image/png": 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"
+    }
+   },
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<img src=\"../../../../images/qiskit_header.png\" alt=\"Note: In order for images to show up in this jupyter notebook you need to select File => Trusted Notebook\" align=\"middle\">"
+    "![qiskit_header.png](attachment:qiskit_header.png)"
    ]
   },
   {
@@ -50,7 +55,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.3"
+   "version": "3.7.5"
   },
   "varInspector": {
    "cols": {
diff --git a/qiskit/advanced/aqua/optimization/max_cut_and_tsp.ipynb b/qiskit/advanced/aqua/optimization/max_cut_and_tsp.ipynb
index ef97d19ef..2bc554143 100644
--- a/qiskit/advanced/aqua/optimization/max_cut_and_tsp.ipynb
+++ b/qiskit/advanced/aqua/optimization/max_cut_and_tsp.ipynb
@@ -1,25 +1,22 @@
 {
  "cells": [
   {
+   "attachments": {
+    "qiskit_header.png": {
+     "image/png": 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"
+    }
+   },
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<img src=\"../../../../images/qiskit_header.png\" alt=\"Note: In order for images to show up in this jupyter notebook you need to select File => Trusted Notebook\" align=\"middle\">"
+    "![qiskit_header.png](attachment:qiskit_header.png)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# _*Qiskit Aqua: Experimenting with Max-Cut problem and Traveling Salesman problem with variational quantum eigensolver*_ \n",
-    "\n",
-    "The latest version of this notebook is available on https://github.com/Qiskit/qiskit-tutorial.\n",
-    "\n",
-    "***\n",
-    "### Contributors\n",
-    "Antonio Mezzacapo<sup>[1]</sup>, Jay Gambetta<sup>[1]</sup>, Kristan Temme<sup>[1]</sup>, Ramis Movassagh<sup>[1]</sup>, Albert Frisch<sup>[1]</sup>, Takashi Imamichi<sup>[1]</sup>, Giacomo Nannicni<sup>[1]</sup>, Richard Chen<sup>[1]</sup>, Marco Pistoia<sup>[1]</sup>, Stephen Wood<sup>[1]</sup>\n",
-    "### Affiliation\n",
-    "- <sup>[1]</sup>IBMQ"
+    "# Qiskit Aqua: Experimenting with Max-Cut problem and Traveling Salesman problem with variational quantum eigensolver"
    ]
   },
   {
@@ -97,8 +94,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
+   "execution_count": 2,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:59:38.109248Z",
+     "start_time": "2019-12-12T17:59:38.098188Z"
+    }
+   },
    "outputs": [],
    "source": [
     "# useful additional packages \n",
@@ -108,7 +110,7 @@
     "import numpy as np\n",
     "import networkx as nx\n",
     "\n",
-    "from qiskit import BasicAer\n",
+    "from qiskit import Aer\n",
     "from qiskit.tools.visualization import plot_histogram\n",
     "from qiskit.aqua import run_algorithm\n",
     "from qiskit.aqua.input import EnergyInput\n",
@@ -136,8 +138,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
+   "execution_count": 3,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:59:38.838143Z",
+     "start_time": "2019-12-12T17:59:38.836008Z"
+    }
+   },
    "outputs": [],
    "source": [
     "from qiskit import IBMQ\n",
@@ -153,21 +160,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
+   "execution_count": 4,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:59:40.984512Z",
+     "start_time": "2019-12-12T17:59:40.783157Z"
+    }
+   },
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/rchen/.pythonVirtualEnv/quantum-dev/lib/python3.7/site-packages/networkx/drawing/nx_pylab.py:579: MatplotlibDeprecationWarning: \n",
-      "The iterable function was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use np.iterable instead.\n",
-      "  if not cb.iterable(width):\n"
-     ]
-    },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -194,8 +197,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
+   "execution_count": 5,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:59:41.709471Z",
+     "start_time": "2019-12-12T17:59:41.702848Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -230,8 +238,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
+   "execution_count": 6,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:59:42.618252Z",
+     "start_time": "2019-12-12T17:59:42.418721Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -259,7 +272,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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ZZ/D395fpmBogoauTCxcusHTpUn788UciIiJ47LHHpI0gdNezZ098fHyu3QRz5syZMiljZvJTXsuKiopYs2YNQ4YMoWnTpqSlpfHEE09I4Io6o02bNqxfvx6j0ShjZTVAftJriaqqfPzxxwwePJgDBw6QmJhIZGQkzs7OepcmxD84OTnx4osvMmjQIEaMGMFXX32ld0kWQ9oLtWD//v3ExcWRl5fH888/T9euXfUuSYibUhSFoKAgbrvtNmbMmMGePXtkrMwM5F+vBl2+fJkXXniBiRMn0r9/f5KSkiRwRb1zdaxs165dREZGkpmZqXdJ9ZqEbg0oLi5mw4YNBAUF4erqSmpqKoMGDZIVgqi3mjRpwrJly/D19SU0NJQ9e/boXVK9Je0FM1JVlS+++IIlS5bg4+PDunXraNOmjd5lCWEWNjY2TJgwAT8/P6Kjoxk7diwBAQEyVlZJErpmcujQIeLi4sjIyODZZ5/lvvvu07skIWpEjx498Pb2vjZWFhsbK2NllSDvd6spMzOT+fPnM378eHr37s3mzZslcIXFuzpWpqoqYWFhnDp1Su+S6g0J3SoqKSkhKSmJwMBAHB0dSUlJITAwEBsbG71LE6JWODk58cILL+Dv78/IkSNlrKyCpL1QSaqq8vXXX7NkyRJat27Nm2++Sdu2bfUuSwhdXB0r+/vVymTxUTZFVdUyH7znnnvUn3/+uRbLqduOHj1KXFwc58+fZ/LkyfzrX//SuyQh6ozMzExiY2MBmDdvHk2aNNG5Iv0oivKLqqr33OgxaS9UQHZ2NgsXLmTs2LE89NBDbNmyRQJXiL9p3LgxS5cu5fbbb5exsnJI6JajtLSUzZs3ExAQAEBKSgpDhgzB1la6MkLciI2NDZGRkUybNo3o6Gi2bdtGee+mrZGkRxm+/fZb4uPjadasGatXr8bb21vvkoSoNx5++OFrY2W7d+9m5syZcn3oP8hK92+OHz9OVFQUixYtIioqiqVLl0rgClEFrVu3Zv369QCMGDGCkydP6ltQHSGh+4ecnBzi4uIIDw/n/vvvJzk5me7du8vZNkJUg6OjI3PnzmXw4MGMGjWKL774Qu+SdGf17QWj0UhaWhpvvPEGPXr0YNu2bTRu3FjvsoSwGIqiMHjwYG677TamT5/O7t27iYiIsNqxMqte6f74448EBwfz6aefsmzZMmJjYyVwhaghnTt3JikpiX379hEZGUlGRobeJenCKkP35MmTTJ48mXnz5jFu3DhWrFhBhw4d9C5LCIt3dazMz8+PkJAQdu3apXdJtc6qQvfKlSssWbKEESNGcOedd7Jt2zZ69uwpfVshapHBYCAiIoIZM2YQExPD1q1brWqszCpC12QykZaWhr+/Pzk5OWzdupXhw4djb2+vd2lCWK3u3buzdu1a3n77bebMmUNBQYHeJdUKiw/dX375hZCQEN5//30SEhKYM2cOt9xyi95lCSHQxsrWrVuHwWAgLCzMKsbKdJ1eKCyE9HQoKQF7e2jVSvvVHM6cOcNrr73Gvn37mDhxIr1795Y2ghB1kKOjI88//zxpaWmMHDmSZ599lp49e+pdVo2p9dA9fhw2b4aPP4YTJ8DWFhQFVBWMRvD2hr59YcgQLYQrKz8/n7Vr15KWlkZISAgvvvgiDg4OZn8eQgjzURSFgICAv4yVjR8/3iLHymrtKmPp6RAbC//7nxawjo7g4ADX3zbMZNJWv0VFWhD36QNz54KHx833bzKZeO+991i+fDn3338/kZGReFTkC4UQdUpWVhaxsbGYTCbmz59fL69WVt5VxmoldFNTYdYsrY3g4vLXoC2LyQQ5OeDkBIsWaavfsvz2228sWrQIe3t7YmJiuP3226tdsxBCPyaTiZUrV/Lee++xYMEC7rjjDr1LqhRdQ3fpUoiPhwYNtJVtZRUWah/PPQehoX997OzZsyQkJLBr1y4mTJhA3759pW8rhAX56quvePHFFwkPDycoKKje/Hzrdj3dLVtg8WJtdVvVtqqjoxbYc+fChx9qf5afn8+KFSsYOnQo7dq1IzU1lX79+tWb/xAhRMV0796ddevW8e677zJr1qwqjZWpKmRmwpkzcO4cFBfXQKGVUGMr3RMntJaAvb15JhIKC0FRVGbO/JSkpHi6dOnChAkTaNasWfV3LoSo0woLC1mwYAF79+5l0aJFtGnTptztCwrggw/g7bdh9264cgVsbLQANpm0A/YPPQRPPw3t25u/Xl3aC089Bb/8Am5uVfryfygoyOfkyVxatNhDSopHvevxCCGqR1VV3n77bZYvX05sbCy9evX6xzYlJbBqFaxYoa1oFUU7LnR1Sgq00C0qurqQgy5dYN48MOeVAMoL3RoZGdu7VwtcV9fq76u0tITz5y+Qn59Hs2ZNMZl60aSJtBGEsDaKouDv789tt93GtGnT2LNnz1/Gyg4dgshIOHxYa0mWlT8GgxbETk7aynfHDhgwACZPhjFjKnagvzpqZPcbN2pP5mYtVpOpmPT0Fzh8+DEOHOjO0aPBXLnyLQCqauLixYscPXoUe3s7fHx8cHNzQ1UVNm+uiaqFEPVBp06dSEpK4sCBA0RERJCRkcHOnTBoEBw9qr27rmhLU1G0cHZygoULYdo0bSVck2okdD/5RHuluTkjdnbNadNmNR07foGHxzOcOTODjIz9HD58hOLiItq1a4eHR1MMBu3VzNER/vvfmqhaCFFfuLm58frrr3PXXXfh7x/D4MEFlJZqAVqV4+l2dlpYv/02zJ9v/nqvZ/bQzcjQjhTa2VXgLzc44eExBnv7liiKATu7eyguduXSpZ9p1aolrVp5Ymf315csBwc4dkzryQghrJfBYGDMmGcoLV3EmTMZFBdnAFW/WpnBoE1arV8P339vtjL/+feYe4fHj2uBW5lXm9LSEtLT0zl5cjeKch4vr3/j7HzjpbLBoDXFT582T71CiPorMRHOnbuF9u09yM7O4syZM5hMxirvz8ZGy69Jk2puYWf2A2lXT+GtjMOHD1NUlI+iLMFgeJBTp0qBwyiK1jzX5m+VPz6H0lIH5sxZS9Oml7CxscHW1vbar3//vKzHzLn9jbazsbGRuWEhalBpKSxbprUc7e3tadu2LWfPnuP48eN4enpib1+1kwMaNIDLl+Gjj+Dxx81cNDUQulWZyfX09OTYsanY2zvTrt08FMUGVeWPCxurqKqKNtmmfZ6XZ+CJJx6lRYsrlJaWYjQaKS0t/cvnN/qzoqKiSm1//cfft7vZ9iaTCYPBUCfCvzLbl/Vnf3/MYDDIi4rQ1VdfQXa21hIAUBQDLVu2ICsri+PHj9OiRQsaNdIeNBpzOHv2Ba5c+R5bWzc8PCJxde1X5r5tbLTRs3oRuq1ba7NyFZleAC1Yc3LiadjQiKJM5dy5i7Rq1RIbmxuXpqraavrxx7vg7Gzm4s1IVVWMRmO54VzZ8L/Z9oWFhdV+sShvu+v/zGQyVSicazr8a2r7P99hibrqk0+01e5fKbi5NcbR0ZHTp09TUFBA06ZNOXduAYpiR8eOH1FYeJBTpybi6NgRBwfvG+67QQPYt08LdXOMvl7P7KHr4QENG2r/GBU5mHbu3MsUFx/Dy2s5BoMTFy5c4NixY3h6euLo6PSP7YuKwNOTOh24oLVFrv4AW+KlJU0m019eVMz9TuFG2xUUFFT7xaKi25tMpnr5YlHedtc/ZqjpYdRa8PPP2qjXjTg6OtGuXTvOnEnn+PEDFBR8io/PNgwGZ5yd76JRo4fJzv5/NG064YZfryhafu3bB926mbdus4euosDDD8N778HNbqxbUnKWrKw0FMWeQ4f+vIyYq+skTp400bRpU9zc3IA/VxwFBdC7t7mrFpVlMBgwGAzYVeSVtR4ymUw1Eublbf/39ld529/shay87UtLS68tCvQO/6q20mxsbDh0qBmNGmnX4f7zuM+fn9vY2NKmTWvS078nK6uE0lL3a+1PB4cO5Of/Wu73QEmJNvdb50MXYPhweP/9m7cY7Oxa4Ot749OMmzQp4vTp0+TnF9CiRXMUxYCqatMLQ4fWRNVC/MlgMFj0PfSuvqjU1DuFv29fUlLyj3cqVX1x0T5MnDu3mkuXslGUP4/7XD0OdH0AG43nUFVHjhw5jJ+fH6BgMDTEZMq7yb+RdqqwudVI6N59t3Ye86FDVe+H2Ns7/HE08uy1o5F5efbcfz/4+Ji3XiGszdUXlfr8wuLtDY0bN7vBwk6b1VVVlaKiYtLT0ykuLqR16zZcfddsMuVhMJR/BtfVFoO51UhjR1EgLk77/J+N7oozGGxo1aoVbm5uHDlympKSfF5+2Tw1CiHqt5Yty5qlVTCZtMsInDx5gkaNOuDgYI+zc861LYqKDpZ5EO0qe3vt+JG51Vg33ddXGzDOza3uucwKLi5NcHNrRePGCbz33nJMNX1ytBCizrv77hu9/VfJysrkyJEjGI1GvL298fBojYtLLy5eXInJVEB+/k5yc7/E1XVAmftWVW3B6Otr/rpr9BBmRAQEB2tjF1Vd8RYXa7ftmTzZgU8/HcPOnTuZMGECWVlZ5i1WCFGv9Oz512NG+fl5HDt2jKysbFq3bk2LFi2xtdX6A82bz8BkKuLgwUc4cyaW5s1nlrvSLSzUJrFq4nLdNX67HlXVzhpJSNB+36hRRed3tbC2sYFnn9Vu1aMoYDQaWbZsGR999BGvvPKK3A9NCCuVnw/33AOKUszlyxcoLNRmcl1cXLh+4qkqsrK03Bkxompfr9vterS/XLvG5TvvaAfAcnK0J3T1BIrrqar255mZ2nZ33KFd/X3YsD+D2sbGhqioKGJiYpg0aRKpqamU98IhhLBU+fj4fMfRoxdxdHTAx8cHFxdXqhu4RUXahbUGDjRPlX9XaxPSnTppY2RbtsCjj/65ks3L+/MjO1sbCRs4ENLStLsIe5fxDqBnz56sWbOGrVu38vzzz1NYE7MdQog6x2QysX37dgICAujU6VNuu60pTk4eKEr140xVtRX0nDk3P8+gqmrlFuw3oqpw/jycPKmtbu3twctL66NU5uzLgoIC5s+fz+HDh1m4cCGtW7eukXqFEPrbuXMncXFxGAwGYmJi8PPz47vvtHfDTk7Vux/j1YVgt27ajRiqc9Kerrdgrw2qqrJt2zbeeOMNZs+eTffu3fUuSQhhRufOnSMhIYHffvuNyMhI+vXr95dTmdPSYOpULXgdHSu/f1XV2p6dOsHmzdqlDKpD155ubVAUhaCgIOLj41mwYAHLli2TsTIhLEBBQQGrVq0iODiYNm3akJqaSv/+/f9x7Qh/f+2A/dXwrMxhnsJCbYXbs6d5AvdmLCJ0r/Lz8yMpKYk9e/YQGRlJZmam3iUJIapAVVU++OADAgICOHHiBJs2bWLcuHE4lXWFG6BfP/j0U3jwQS1EMzO11uWNmEzabdmvHkdavBjeeKPmAxcspL3wd0ajkRUrVvDBBx+wYMGCP863FkLUB7///juLFi2itLSUmJgY7rrrrkrvY/9+rS/73nvagTFb2z+vBXP9iQ+jR0OfPmVfrayqLL6nW5Yvv/ySl156idGjRxMYGCjXRxWiDrtw4QJLly7lp59+IiIiggEDBlT7EpSqCufOabdlvxq+zZpp14apySuulhe6NXLBm7ri4Ycfxtvbm2nTprFnzx5mzpxZ7tsTIUTtKyoqIikpibfeegt/f39SU1NxNtMFsxUFWrTQPuoKi+rp3kjr1q1Zt24dAGFhYZw8eVLnioQQoPVtP/74YwYPHsyBAwdITExk/PjxZgvcusqi2wvXU1WV1NRUVq1axbPPPkuPHj30LkkIq7V//37i4uLIy8sjJiaGrl276l2SWVlte+F6iqIwePBgbrvtNqZPn87u3buJiIjAxsZG79KEsBqXL19m2bJl/O9//2PcuHEMHDjQIm4dVBnW9WyBzp07k5SUxL59+xg/fjwZGRl6lySExSsuLmbDhg0EBQXh6upKWloa/v7+Vhe4YIWhC9C4cWOWLl3KHXfcQUhICLt27dK7JCEskqqqfP755wQGBrJz507WrVvHxIkTaVgbA7F1lNW0F/7OYDAQERFB586diYmJITw8nKCgIBkrE8JMDh06RFxcHBkZGcTGxnL//eDxd9sAABDmSURBVPfrXVKdYDUH0spz+vRppk2bRrt27Zg1a5aMlQlRDZmZmaxYsYIvvviC0aNH4+/vb3XHTiz+2gvV5enpybp167Czs2P48OEyViZEFZSUlLBp0yYCAwNxcHAgJSWFwMBAqwvcm5HQ/YODgwPPPfccQ4YMYdSoUXz22Wd6lyREvaCqKl9//TVPPfUUP/zwA2+++SYxMTF/3MFB/J3V9nRvRFEU/P39/zJWFhkZKa/UQpTh6NGjxMfHc/bsWaZMmcK//vUvvUuq82SlewOdOnUiMTGRQ4cO8cwzz3D58mW9SxKiTsnOzmbhwoWMGTOGf//73yQnJ0vgVpCEbhnc3NxISEiga9euhIaGsnPnTr1LEkJ3paWlJCcnM3jwYFRVJSUlhaeffhpbW3nTXFHyL1UOg8HA2LFj6dy5M1OnTmXEiBEMGTJExsqEVfruu++Ij4/Hw8ODlStX4uPjo3dJ9ZKMjFVQeno6U6dOxcvLi1mzZln8RTmEuOrEiRMsXryYEydOEB0dzUMPPSQLj5uQkTEzaNmyJWvXrsXR0ZHhw4dz/PhxvUsSokbl5uYSHx/PyJEj6dq1K8nJyXTv3l0Ct5okdCvBwcGBOXPmMHToUMLDw/n000/1LkkIszMajaSmphIQEEBBQQHbtm0jNDQU++rcaldcIz3dKhg4cCC33nor06dPZ9euXUyYMEEOJAiL8NNPPxEXF4eLiwtLly6lY8eOepdkcaSnWw3Z2dnMnj2bgoICXn75Zdzd3fUuSYgqOXXqFK+99hoHDx5k0qRJ9OzZU9oI1SA93Rri6urKkiVLuPfeewkNDWXHjh16lyREpeTl5ZGQkEBYWBi33347KSkp9OrVSwK3Bsl74moyGAyMGTOGzp07M336dIYPH05wcLB804o6zWQysX37dlasWMEDDzxAcnKyvFOrJdJeMKP09HSmT59Oq1atmDNnjoyViTppx44dLFq0CEdHR2JiYujUqZPeJVkcaS/UkpYtW7JmzRoaNmzIsGHDOHr0qN4lCXFNeno6M2bMYPbs2QwfPpw333xTAlcHErpmZm9vz6xZsxg2bBhjxozho48+0rskYeXy8/NZvnw5ISEh+Pj4kJKSQp8+faQFphPp6daQJ554gltvvZWpU6eye/duJk6cKGNlolaZTCbef/99li1bxj333MOWLVto2rSp3mVZPenp1rCcnBxmz57NlStXWLBgAR4eHnqXJKzArl27WLRoEYqiMGXKFPz8/PQuyapIT1dHLi4uLF68mAceeIDQ0FB+/fVXvUsSFuz8+fPMmjWLGTNmMGTIENatWyeBW8dI6NYCg8FAeHg4zz33HDNmzCAxMZHy3mEIUVmFhYWsXr2ap59+mlatWpGSkkL//v2t8hbndZ38j9SiBx54gA0bNvDxxx8zffp08vLy9C5J1HOqqvLf//6XgIAAjh07xqZNm3jmmWdkXLEOk9CtZS1atODNN9/Ezc2N0NBQGSsTVbZ3715GjRpFYmIiL730Ei+//DItWrTQuyxxE3I4XQf29vbExsayfft2xowZw9SpU+nbt6/eZYl64uLFiyxdupQffviBiIgIHnvsMWkj1CMSujp6/PHHr42V7dq1i0mTJmFnZ6d3WaKOKioqYtOmTWzatIlBgwaRlpYmbYR6SF4eddaxY0cSExNJT09n7NixXLhwQe+SRB2jqiqffPIJgYGB7Nu3j40bNxIZGSmBW0/JnG4dYTKZWL9+PVu3buWll17inntuOOInrMyBAweIi4sjNzeXmJgY+b6oJ8qb05X2Qh1hMBgYOXIknTt3JjY2lqFDhzJs2DA5VdNKZWRksHz5cr766ivGjRvHwIEDpW9rIeR/sY6577772LhxI5999hlTp07lypUrepckalFxcTEbN24kMDCQBg0akJqair+/vwSuBZH/yTqoefPmvPHGG7i7uzNs2DAOHz6sd0mihqmqyhdffEFQUBC//fYb69atIzo6mkaNGuldmjAzaS/UUfb29syYMYP333+fcePGERMTw6OPPqp3WaIGHD58mPj4eC5dusSMGTPo1q2b3iWJGiShW8f179+fDh06MG3aNHbv3k10dLSMlVmIzMxMVq5cyeeff054eDgBAQHY2NjoXZaoYdJeqAc6dOjAxo0bOX/+PKNHj5axsnqupKSEt956i8DAQOzs7EhJSSEoKEgC10pI6NYTjRo14tVXX6VHjx6Ehoby448/6l2SqIJvvvmGp556iu+++4433niDKVOm4OLiondZohZJe6EeMRgMhIWF0alTJ2bPns2QIUMYPny4HNmuB44dO8bixYs5ffo0kydP5t///reMA1op+Wmth+677z4SExP56quvmDJlCrm5uXqXJMqQk5PDq6++yujRo+nWrRvJyck8+OCDErhWTEK3nmratCmrV6+mefPmDBs2jEOHDuldkriO0Whk69atBAQEYDQaSUlJITg4WA6CCmkv1Gd2dnZMmzaNDz74gGeeeYbo6GgGDBigd1lW7/vvvyc+Ph53d3dWrFhB+/bt9S5J1CESuhbg0UcfvTZWtmvXLmJiYrC3t9e7LKtz8uRJFi9ezLFjx4iOjqZ79+7SRhD/IO0FC9G+fXs2btxIRkYGo0eP5ty5c3qXZDVyc3NZvHgxI0aM4O6772br1q08/PDDErjihiR0LUjDhg1ZuHAhvXv3ZtiwYfzwww96l2TRTCYTaWlpBAQEkJeXx7Zt2xg2bJi8yxDlkvaChVEUhWHDhtGpUyeeffZZnnrqKcLCwmSszMx+/vln4uLiaNSoEa+//jq33nqr3iWJekKup2vBLly4wIwZM3B1dWXu3LkyhG8GZ86cYcmSJRw4cICJEyfSq1cvaSOIfyjverqy/LFgTZs2ZdWqVbRq1YrQ0FAOHjyod0n1Vn5+Pq+//jrDhg3D19eXlJQUevfuLYErKk1C18LZ2dkxZcoUIiIiiIiIYPv27XqXVK+YTCb+85//4O/vz+XLl9myZQsjR46Uvq2oMunpWom+ffvSoUMHpkyZwu7du5kyZYoEx03s2LGDuLg47O3tiY+Pp1OnTnqXJCyArHStiLe3N4mJiWRlZREeHs7Zs2f1LqlOOnv2LDNnzmTWrFmEhoayZs0aCVxhNhK6VqZBgwa88sor9OnTh+HDh/Pdd9/pXVKdkZ+fz4oVKxg6dCjt2rUjNTWVvn37St9WmJW0F6yQoiiEhIRcGyvz9/dn1KhRVjtWZjKZ+PDDD1m6dCldunRh8+bNNGvWTO+yhIWSkTErd/HiRWbMmEHDhg158cUXrW6sbPfu3cTFxWEymZgyZQp33HGH3iUJCyAjY6JMHh4erFq1Ci8vL0JCQti/f7/eJdWKCxcuMHv2bKZPn05gYCDr16+XwBW1QkJXYGtry+TJk5kwYQKRkZG8++67epdUYwoLC3njjTcYMmQILVq0ICUlhQEDBlhta0XUPunpimseeeQRfHx8rl2tbPr06RYzVqaqKh9//DEJCQl07tyZpKQkWrZsqXdZwgrJy7v4C29vbzZu3EheXh4jR44kPT1d75Kqbe/evYSHh7NhwwZeeOEFFixYIIErdCOhK/7B2dmZl19+mf79+xMWFsa3336rd0lVcunSJebOnUt0dDRPPPEEiYmJdOnSRe+yhJWT9oK4IUVRCA4OxtfXl9jYWAYNGkR4eHi96H0WFxezadMmkpKSePLJJ0lLS6NBgwZ6lyUEICtdcRN33303iYmJ/PTTT0yaNIns7Gy9SyqTqqp89tlnDB48mN9//50NGzYQFRUlgSvqFAldcVNX7/Xl7e1NSEgIe/furdqOjEbIzITLl6GgwKw1Hjx4kLFjx7J69Wpmz57NokWL8PT0NOvfIYQ5SHtBVIitrS2TJk3Cz8+PqKgoxo8fz8CBA29+iuzhw7BtG/zvf3DoEJhMoChaALdoAV26wKBB8PDDYGNT6boyMjJYsWIFX375JWPHjmXgwIHYVGE/QtQWOSNNVNrx48eZOnUqfn5+TJ8+HQcHh39utH8/zJkDv/4KqgoODuDoCFd7wqoKxcXaitdgAFdXmDoVBg/+c5tylJSUsGXLFtavX8+AAQMYPXo0jRo1MvMzFaJq5Iw0YVZt27Zlw4YNFBYWMnLkSM6cOfPng0YjJCTA44/Djh1amDZuDM7Ofw1TRdGC2M0NXFy08J05E4YOhXKufqaqKl999RVBQUH88ssvrF27lsmTJ0vginpDVrqiylRVJTk5mbVr1zJnzhwe7NYNJk2CDz6ARo3AtpLdK1WFrCy45RbYuhXatv3Lw0eOHCEuLo6LFy8yefJkHnjgAfM9GSHMqLyVroSuqLadO3cyc+ZM5pWWctfhwyhubhVqEZQpOxuaNIH/9//A3Z2srCxWrVrFJ598wujRo/H398e2soEuRC2S9oKoUXfeeSdbwsNp9f33nMrNpbScF/IKcXWFy5cxzZjB5rfeIjAwEIPBQGpqKkFBQRK4ol6T715RfTk5uLz4Io1at+ZCbi7Hjh3D09MTJ0fHKu/yio0NVzZv5vKFC6xatQpvb28zFiyEfiR0RfX95z+Qm4vi5kYzJyecnJw4efIkTZs2xc3Njcrcd6GouJjz589TXFxMC3d3xhcVobRrV2OlC1HbJHRF9ZhMsGoVXHc1MpdGjXBwcOD06dMU5OfTvEULDIrC1owMtmdnc7ioiL4uLjx/3UVnjEYjFy9dIjs7G3d3dzw9PbXe19GjsGcP+PnV/nMTogZIT1dUz6lTcP48ODn95Y8d7O1p17Ytqqpy/PhxiouLcbe1ZZS7O0+4ul7bTgUyMjM5cuQIqsmEj48PtzRpgkFRtLGy0lL48staflJC1BwJXVE9+/Zp4XiDM9MMBgMtW7XCzc2N48ePc6/BQI9GjXD944yxK3l5HD16lNzcXNp4edGiRQts/342mb09fP99bTwTIWqFtBdE9Rw6pJ1ZVgYFaNK4MY6Ojpw5fZoCNzdKjUays7M5l59Ps2bNaNioUdl9XwcHOHCgJioXQhey0hXVk5dXoc2cnZxo164dBfn5ZGRkYGdnh7ePD43KC1zQ5n0LC81SqhB1gYSuqB57e+1MsgqwtbWljZcXHh4eODs7a33bm1HVKl0IR4i6SkJXVE+bNmBnV+HNlT8+Kqy4WPs7hLAQErqienx9K7wSNaoqxSYTJsAEFJtMGG+2Si4shPvuq3aZQtQVciBNVE+HDlrolpTcdMW75tIlVl+6dO3372dnM8bdnTEeHmV/kZ0dyIVthAWR0BXVY28PwcGwdq12CcdyjPHwKD9g/66oCBo0gO7dq1mkEHWHtBdE9YWEaKvd0lLz7jc/H8LDK9UzFqKuk9AV1eflBc88A7m5FZ5kuKmcHPD0hFGjzLM/IeoICV1hHuPHQ/v2WlhWV1GR9mtCgnaLHyEsiISuMA97e0hMhGbNtLs/VHXFW1CgTSwsWQJ33mneGoWoAyR0hfk0bQppaXD77VrwlnN68D+oqnZ7doNBu2pZ//41V6cQOpLQFeZ1NXhjY7XQzcrSVq9lrXxLS7Wwzc6GHj3g00+hV69aLVmI2iQjY8L8bGy0A2D+/vDOO9o4WXq61oIwGrVtDAbtWrx2dvDUUxAaqp1oIYSFk9AVNadxYxgxQvvIzob9++HiRS1sGzbUTqxo1ap6N7EUop6R0BW1w9UV7r9f7yqE0J0sMYQQohYpajmjPYqiXARO1F45QghhEbxUVb3hOe/lhq4QQgjzkvaCEELUIgldIYSoRRK6QghRiyR0hRCiFknoCiFELfr/QQEI+LmeO00AAAAASUVORK5CYII=\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -295,8 +308,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
+   "execution_count": 7,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:59:42.814360Z",
+     "start_time": "2019-12-12T17:59:42.806413Z"
+    }
+   },
    "outputs": [],
    "source": [
     "qubitOp, offset = max_cut.get_max_cut_qubitops(w)\n",
@@ -313,8 +331,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
+   "execution_count": 8,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:59:43.518965Z",
+     "start_time": "2019-12-12T17:59:43.130559Z"
+    }
+   },
    "outputs": [],
    "source": [
     "from docplex.mp.model import Model\n",
@@ -333,8 +356,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
+   "execution_count": 9,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:59:43.600910Z",
+     "start_time": "2019-12-12T17:59:43.595066Z"
+    }
+   },
    "outputs": [],
    "source": [
     "qubitOp_docplex, offset_docplex = docplex.get_qubitops(mdl)"
@@ -349,8 +377,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
+   "execution_count": 10,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T17:59:47.642069Z",
+     "start_time": "2019-12-12T17:59:47.435702Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -362,18 +395,9 @@
       "solution objective: 4.0\n"
      ]
     },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/rchen/.pythonVirtualEnv/quantum-dev/lib/python3.7/site-packages/networkx/drawing/nx_pylab.py:579: MatplotlibDeprecationWarning: \n",
-      "The iterable function was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use np.iterable instead.\n",
-      "  if not cb.iterable(width):\n"
-     ]
-    },
     {
      "data": {
-      "image/png": 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un/6kLfsKD7/1a8+dW0VdXS4ORz0mUzfi4mYQE/NYi68PDbXx2msN1NZu5fDhT4iOjsZisfDtb3+bzZs3ExkZ6Up04ee8fT73ZiZNmkRzczNz584lKyur0066EPpyqXT//ndtRNoW3bp9n6CgVRgMwTQ2nuL06dmEhAwhLEybw7Lb7dTV1VJTU0ttbS0Oh4OgoK4kJk5i0aK59OrVy5WoIsDk5+f75Ghx8uTJ2O12MjIyyM7Opl+/fnpHEm7mUukeOtT2hxlCQvpf92/aAt7q6pNUV8dRW1uLzdZIeHg4ERERxMZ2JSQkhKtXFXr3noT0rWiv/Px8nz254eGHH8Zut5Oenk5OTg5J3vK4pnALl0r34kVt1UJbnTu3iYqKd7HbG4AkgoIG0KUL9OzZg7CwMBTlxptfTU1w/rwrCUWgKigo8MmR7jWPPvooDoeDtLQ0XnnlFRISEvSOJNzEpdJtbm7fMrGIiDSqqh6jZ89y4ATdug1GUVqP0NTkSkIRiKqrq7l69Srx8fF6R3HJlClTaG5udhavHOHkH1xaVxUR0fY5XYCYmBgiI6NoaEiiubmMiopftfp6RdGWjwnRHgUFBQwYMMAvlg2mpqby9NNPY7VaKS0t1TuOcAOXfiqHDoWGhva8Q6Fv374oCly9WoHNdqbVV4eEgA//DVHoxNenFr7q8ccfZ/r06VitVi5evKh3HOEil0rXYgGb7dava24up6rqAxyOOkAlOroEm+0j6uv7ATff5UxVtemLoUNdSSgCUV5ens8tF7uVqVOn8p3vfIc5c+ZQVlamdxzhApdK98EHtQcZWtkd8ksKFRW/oqDgm+TnT+TSpd0kJCzDaDRz7lwpNyvemhqtcHv0cCWhCETevNGNK6ZPn05qaipWq5XLly/rHUd0kEs30oYN04qxoEDb7KbFDzF1JSnp1a99PSbGwZkzZygtLf1y39p/7wXpcIDV6ko6EYjsdjtFRUUMHDhQ7yidYsaMGTQ3N2O1Wnn11VeJjY3VO5JoJ5fvNCxdqk0DtOeG2jWKYiAhIYGmpmbOnz/PtRFvdTX06qWNpIVoj9OnT9OjRw/C2/KYpI965plnmDx5MlarlfLycr3jiHZyuXTvuQemTNE2vekIrXjjsdlsXLhwgeZmFbtd20gnJMTVdCLQ+NtNtJbMmjWLiRMnkp6eTmVlpd5xRDu4ZU3NunWQkNDx4jUYjCQkJFBX18iZM1fJzFQZM8YdyUSg8cU9FzpCURTS0tL4j//4D9LT06mqqtI7kmgjt5RuVBS8/Tb07QsVFR2barDZjERHJ5KU9Aeam1+mtbPbhGiJv95EuxlFUZg7dy5ms5mMjAyqq6v1jiTawG2rx3v2hPfeg0cf1Ua8NTVtWdWgnY9WUaH988svG/jrX7/J4cOH2Lt3rxSvaLe8vLyAmF64RlEU5s+fz+jRo5k7dy41NTV6RxK34NZHdqKjYdcu+OEPtSPVq6rgyhWtgJubtRJ2OKCxUSvmykrtNODHHoO//EX7PTo6iqysLP7+97+Tk5PjznjCz5WXl2Oz2QJuRzpFUVi0aBHDhw8nMzOT2tpavSOJVnTKc5KTJsEf/gC//jWkpcFtt2mlW1GhFXFEBDzwgDYXvH8/bN+ujZSviY6OJisri7/+9a+89tprnRFR+KFrN9GUWx1D7YcURWHJkiUMGjSIefPmUVdXp3ck0YJOO5taUeD227VfHREbG0t2djazZ8/GZDLxve99z635hP8JlJtoLTEYDCxbtowNGzawYMECdu3aRVhHT3cVncardwSJi4sjJyeH9957jzfffFPvOMLLBdJNtJYYDAZWrlxJnz59WLRoEQ3t2xxFeIBXly5A9+7dycnJ4Z133uEXv/iF3nGEF/PV0yLczWAwsHr1arp168bixYuxtWWDFOExXl+6AD179iQnJ4ef//zn/OpXrW8HKQKTzWajpKREzhX7ksFgYO3atURHR/Pcc89J8XoRnyhdgN69e5Odnc2Pf/xj3n33Xb3jCC9TXFxMfHy8nAp9HaPRyPr16wkNDWXp0qU0yYkAXsFnShegb9++ZGdn8+qrr/L+++/rHUd4kUC/idYSo9HIpk2bMJlMLFu2jObmZr0jBTyfKl2AxMREsrKy2LdvH7///e/1jiO8hNxEa5nJZGLz5s2oqsqKFSukeHXmc6ULkJyczL59+9i5cyd/+tOf9I4jvIDcRGtdUFAQW7ZsoaGhgdWrV2O32/WOFLB8snQB+vfvz969e9m2bRv/93//p3ccoSNVVWV6oQ2Cg4N56aWXuHr1KmvWrMHRkU1ShMt8tnQBBg0axJ49e9iyZQsff/yx3nGETi5duoTJZCIuLk7vKF4vODiYHTt2cOXKFdatWyfFqwOfLl2AIUOGsHPnTtavX8/f//53veMIHQTaJjeuCgkJYceOHZw/f54NGzZI8XqYz5cuwPDhw9mxYwdr167lk08+0TuO8DC5idZ+YWFh7Ny5k5KSErZs2SLF60F+UboAI0eOZNu2baxcuZIDBw7oHUd4kMzndkx4eDi7d+/m5MmTbNu2TbZS9RC/KV2A0aNHs3XrVpYvX87hw4f1jiM8REq3464V7+eff8727duleD3Ar0oXYOzYsWzatIkf/OAHHDt2TO84opPV19dz8eJFkpKS9I7is7p06cLevXs5evQoO3fulOLtZH5XugBms5kXXniBxYsXc/z4cb3jiE5UWFhIcnIyJlOn7VIaECIjI9m3bx8HDhxgz549UrydyC9LF+DOO+9kzZo1LFy4kBMnTugdR3QSmVpwn6ioKLKzs/nnP/9Jdna2FG8n8dvSBbjrrrt4/vnnmT9/Pvn5+XrHEZ1AnkRzr2untnz44Yf88Ic/1DuOX/Lr0gW49957WbZsGZmZmRQWFuodR7hZfn4+Q4YM0TuGX+natSs5OTl88MEHvP7663rH8Tt+X7oAkyZNYvHixWRkZFBcXKx3HOEmDoeDkydPyki3E8TGxpKTk8Nvf/tbfvKTn+gdx68EROkCTJ48mXnz5pGens7p06f1jiPcoLS0lMjISKKiovSO4pe6detGTk4O7777rhyX5UYBU7oA3/zmN0lLSyMtLY0zZ87oHUe4SG6idb4ePXo4j8t666239I7jFwKqdAGmTJnCs88+S1paGqWlpXrHES64duS66FzXjst66623+OUvf6l3HJ8XcKULkJqayowZM7BarVy4cEHvOKKD8vLyZKTrIdeOy/rpT3/Kb37zG73j+LSALF2AJ554gqlTp2K1WikrK9M7jugA2ejGs64dl/Xaa6/x3nvv6R3HZwVs6QJMmzaN1NRUrFYrly9f1juOaIfq6moqKyuJj4/XO0pASUhIICcnh1deeUXOKeyggC5dgBkzZvDoo49itVopLy/XO45oo4KCAgYOHIjBEPA/wh537ZzCvXv3yjmFHSA/scAzzzzD5MmTsVqtVFRU6B1HtIHcRNNXcnIyWVlZ7Ny5kw8++EDvOD5FSvdLs2bNYuLEiWRkZFBVVaV3HHELslxMf9fOKXzppZf4y1/+onccnyGl+yVFUUhLS2P8+PGkp6dTXV2tdyTRCild7zBo0CD27t3Liy++yIcffqh3HJ8gpXsdRVHIzMxkzJgxzJ07l5qaGr0jiZuw2+0UFRUxcOBAvaMIYPDgwezatYtNmzbJAbFtIKX7FYqisGjRIoYPH868efOoq6vTO5L4itOnT9O9e3fCw8P1jiK+NGzYMF5++WXWr1/PP/7xD73jeDUp3ZtQFIUlS5YwYMAA5s+fT319vd6RxHVkfa53GjFiBNu3b2fNmjVyQGwrpHRbYDAYWL58OQkJCSxcuJCGhga9I4kvyXyu97r99tudB8Tm5ubqHccrSem2wmAwsHLlSnr06MHixYux2Wx6RxLISNfbXTsgdsWKFRw6dEjvOF5HSvcWDAYDa9asITo6mueee06K1wvIaRHeb+zYsWzevJkf/OAHHDlyRO84XkVKtw2MRiPr168nNDSUZcuW0dTUpHekgFVeXk5jYyO9evXSO4q4hZSUFDZu3MjSpUvlZO7rSOm2kdFoZNOmTSiKwooVK2hubtY7UkC69iSaoih6RxFtYLFYWLduHYsXL+azzz7TO45XkNJtB5PJxObNm7HZbKxatQq73a53pIAjN9F8z4QJE1i9ejULFy7k888/1zuO7qR02yk4OJht27ZRXV3NmjVrcDgcekcKKHITzTfdfffdrFy5kgULFpCXl6d3HF1J6XZAcHAw27dv58qVK7zwwgtSvB4kN9F81/Unc+fn5+sdRzdSuh0UEhLCjh07KC0tZdOmTVK8HmCz2SgpKWHAgAF6RxEdNGnSJJYsWUJmZiaFhYV6x9GFlK4LwsLC2LlzJ8XFxWzduhVVVfWO5NdOnTpFfHw8wcHBekcRLnjwwQdZuHAhGRkZFBcX6x3H46R0XRQeHs7u3bv54osv2L59uxRvJ8rLy5OpBT/x0EMPkZmZSXp6OqdPn9Y7jkdJ6bpBREQEe/bs4dixY+zatUuKt5PITTT/8sgjj5CWlkZaWhpnzpzRO47HSOm6SWRkJHv37iU3N5d9+/ZJ8XYCWS7mf6ZMmcKzzz6L1Wrl3LlzesfxCCldN4qKiiIrK4u//e1vvPrqq3rH8Suqqkrp+qnU1FS+//3vY7VaKS0t1TtOp5PSdbOYmBiysrL4y1/+wuuvv653HL9x6dIlTCYTcXFxekcRneDb3/42Tz75JFarlQsXLugdp1NJ6XaC2NhYsrOz+d3vfsdPfvITveP4BVmf6/++853vMHXqVKxWK2VlZXrH6TRSup0kLi6O7Oxs3n33Xf77v/9b7zg+T6YWAsO0adNITU3FarVy6dIlveN0CindTtSjRw9ycnJ4++23efvtt/WO49NkpBs4ZsyYwaOPPkpaWhpXrlzRO47bSel2sp49e5KTk8Obb77Jr3/9a73j+Kz8/HyGDBmidwzhIc888wzf+MY3SEtLo7y8XO84biWl6wF9+vQhOzubH/3oR7z33nt6x/E59fX1XLx4kaSkJL2jCA+aNWsW9913H+np6VRWVuodx22kdD0kPj6e7OxsXnnlFd5//3294/iUwsJCkpOTMZlMekcRHma1WrnrrrtIT0+nqqpK7zhuIaXrQYmJiWRlZbF3717+8Ic/6B3HZ8hNtMClKAoZGRmYzWa/KV4pXQ9LTk4mKyuLl19+mT//+c96x/EJ106LEIFJURTmz5/PmDFjmDt3LjU1NXpHcomUrg769+/Pnj172Lp1K3/961/1juP18vLyZKQb4BRFYdGiRdx2221kZmZSW1urd6QOk9LVyeDBg9m9ezebN2/m448/1juO13I4HJw8eVJKV6AoCkuWLGHQoEHMmzePuro6vSN1iJSujoYOHcrOnTtZv349//jHP/SO45VKS0uJjIwkKipK7yjCCyiKwrJly0hOTmbBggXU19frHandpHR1Nnz4cLZv386aNWvYv3+/3nG8jtxEE19lMBh4/vnn6dOnDwsXLqShoUHvSO0ipesFbr/9drZu3crzzz/PwYMH9Y7jVeQmmrgZg8HA6tWr6d69O4sWLaKxsVHvSG0mpeslxowZw4svvsjy5cs5cuSI3nG8hox0RUsMBgNr164lJiaGJUuWYLPZ9I7UJlK6XuSOO+5g48aNLF26lGPHjukdxytI6YrWGI1G1q9fT1hYGEuXLvWJ4pXS9TJms5l169axePFijh8/rnccXVVXV1NZWUl8fLzeUYQXMxqNbNy4kaCgIJYvX05TU5PekVolpeuFJkyYwOrVq1m4cCFffPGF3nF0U1BQwIABAzAY5MdUtM5kMrFp0yZUVeX555+nubm5xdfW1cGhQ/Dmm7BmDSxbBi+8AO+8A8ePQ2d3ttLaWV7jxo1T5caOfj788EM2bdrE3r17A/Kv2G+//TaFhYWsWLFC7yjCR9hsNpYsWUJ4eDgbNmzAaDQ6v1dYCG+8Ab/6FaiqVq6qCoqi/W40ar9CQ+GZZ2DqVOjRo2M5FEU5pKrquJt9T4YQXmzixIksXbqUzMxMCgsL9Y7jcTKfK9orODiYbdu2UV1dzerVq3E4HDQ2wrZt8PDD8POfQ0gIdOkCXbtCbOy/f4+O1r5ut8Pu3dCHwrUAABFRSURBVHDfffDLX4LD4d6MUrpe7oEHHmDhwoVkZGRQXFysdxyPkiPXRUcEBwezfft2KioqeO65bTz6qMorr0B4uFawt9qsLiQEYmLAYIDly+HZZ8Gdz2DIXnk+4KGHHsJut5ORkUFOTg6JiYl6R+p0drudoqIiBg4cqHcU4YNCQkJYvnwHZvMZbLZy+vWLBZR2XgOCg+Gjj7Tphjfe0L7mKhnp+ohHHnkEq9VKWloaZ8+e1TtOpzt9+jTdunUjPDxc7yjCBzkcsGBBKGFhAzAYqjl//gLQ8v2rliiKNurdvx82bXJPNildHzJlyhRmzpxJWloapaWlesfpVDK1IFzx05/Cp59C164GEhISaGxs/PJo944Vb3S0ttohN9f1bFK6PiY1NZWnnnoKq9XKxYsX9Y7TaeQmmuio6mp48UWIiNAK02AwkpiYQENDAxcuXKQjxWs0anPBK1ZoKx1cIaXrg5544gmmTp2K1WqlrKxM7zidQka6oqP+53+05WDBwf/+mla8ic7z9kClvPyXFBc/xRdf3Elp6dpbXrdLFzh9WhtBu0JK10dNmzaN//qv/8JqtXL58mW947idHLkuOuonP7mxcK+5Vrx1dXWUlZVhMsXRrdtMoqOntOm6iqLNFb/1lmv5pHR92IwZM3jkkUf87pjqiooKGhsb6dWrl95RhI+pr9cegggLu/n3jUateGtqamlsHElk5L0YjdFtvn5YGHzyiWsZpXR93MyZM3nggQf86pjqa9s5Kkr7lvgIkZ8PQUHaqLQlRqOJpKREqquruXSpfX9LDAmBc+e0R4k7SkrXD8yePZu7777bb05LzcvLk6kF0SFXrrReuNcYjSYSE5OorKykpqa6zddXFO2GmivjGyldP6AoCunp6ZjNZjIyMqiubvsPkTeSm2iioxyO1lcXNDc3cfXqVUpLSykuLkZV1Q5tqGS3dzyjlK6fuHZM9ahRo3z+tFRZLiY6KjLyxpGuw2GnpqaaixcvUFRUSFFREdXVVYSFhZKUlMjgwYMID49o8/VVFZqbtc/pKCldP6IoCosXL2bo0KFkZmb65GmpNpuNkpISBgwYoHcU4YOSkmxUVzdQVlbGqVOnKCgooLy8HKPRRJ8+fRg8eDDx8Ql07RpLUJAJh6MJcAAOHA4bqtr6ELapSdu/ISam4xll7wU/oygKS5cuZdOmTSxYsIBdu3YR1tKtXC906tQp4uPjCb7Zmh8hvsLhcFBQUMD+/fs5cOAAR48epaHhDYKDo+jevTvh4WEoys3Hlpcvv87ly686//3q1d/Rrdtsunef3eLn1ddDSoprmWU/XT/lcDhYv349Fy5c4OWXXyY0NFTvSG3y/vvv889//pONGzfqHUV4IVVVOXfuHLm5ueTm5nLgwAFiYmIwm82YzWbuuOMOXnklipwcbUTqblVVsGcPfOMbrb+utf10pXT9mMPhYM2aNVRUVLBjxw6fGD3u2LGDuLg4nn76ab2jCC9RXl7OgQMHnEXb1NTkLFmz2UyPr+w0fvYsTJwIUVHa9ozu0tioPQ6cm3vzhy+u11rpyvSCH7t2WuqqVatYsmQJ27Zt8/rizc/PZ8aMGXrHEDqqq6vjyJEjzimD8+fPM3bsWMxmM9OnT6dfv36truGOj4fUVPj1r9032lVVbW3uunW3LtxbkZFuAGhubmbFihU0Nzfz4osvEhQUpHekm1JVlfvvv5933nmHuLg4veMID2lubuazzz5zjmTz8vIYPny4cyQ7fPjwG47daYuqKrj/fm3zmy5dXM9YUQFjx8Lbb7dt9CzTC4KmpiZ+8IMfOA/wM91q+3wdlJWV8eSTT/LBBx/oHUV0IofDQWFhobNkjxw5QmJiorNkR48e7ZZ7EIcPw7RpWkm6si3z1avaiPndd6FPn7a9R0pXAP8+tC8iIoL169e3e/TQ2f72t7/x1ltvsW/fPr2jCDcrLS3lwIEDzimDiIgIzGYzFouFcePGER3d9v0P2uOf/4SZM8Fm0/bEbc+T5Q6HVrjdumkj3OTktr9XSlc42Ww2Fi5cSFxcHGvXrvWq481/9KMfUV1dzfz58/WOIlxUWVnJwYMHnaPZuro6UlJSMJvNpKSk0KetQ0Y3KCqCBQvg88+1+diwsNbLV1WhpkZ76mzyZNiwQTu4sj2kdMUNGhoaWLBgAb1792bVqlVeU7zLly/n7rvv5pvf/KbeUUQ71dfX8+mnnzpL9syZM4wZMwaLxYLZbGbAgAG6bmBkt2sn+2ZlwYUL2lNlwcHar2tbNjY2aq8zGGDECJg/XzsRuCOxpXTF19TX1zN//nySkpJYvny5VxRvamoqW7dulcMofYDdbuf48ePOKYMTJ04wZMgQ57zsiBEjvPKGrcMBBw9qy74++QROndKeMgsNhaFDwWyGu+6CIUNc+xwpXXFTdXV1zJ07lyFDhrB06VJdRyL19fXcf//9fPzxx155ky/QqapKcXGxcyR7+PBhevfu7SzZMWPGyCGi15F1uuKmwsPD2bNnD+np6ezYsYNFixbpVryFhYX069dPCteLXLx40Vmyubm5BAcHY7FYeOihh1i5ciWx7Z3oFICUbsCLiIhg7969pKWlsXv3bubNm6dL8crOYvqrqqq64ebX1atXnTe/5syZQ9++fWVjeTeQ0hVERkaSlZWF1WolKyuL9PR0j//Hde20COE5jY2NHD161Fmyp06dYtSoUZjNZjZv3sygQYO8Yq7f30jpCgCioqLIyspizpw5BAUFMXt2yzstdYb8/Hzuv/9+j35moHE4HJw4ccJZssePH2fgwIGYzWYWLFjAbbfd5vWPifsDKV3hFBMTQ3Z2NnPmzMFkMvHMM8945HOvbc8n0wvupaoqp0+fdpbsoUOH6N69OxaLhe9+97vccccdRES0fQNv4R5SuuIGsbGxZGdnM3v2bEwmk0c2nyktLaVLly5ERUV1+mf5u0uXLt3w5JeiKJjNZu6//36WLVtGt27d9I4Y8KR0xdd069aNnJwcZ/FOmzatUz8vPz+fIa4ujAxQNTU1HDp0yDmavXLlCuPGjcNsNjNz5kwSEhLk5peXkdIVN9WjR48biveJJ57otM+Sm2htZ7PZOHbsmLNki4qKuO2227BYLKxbt46hQ4fKzS8vJ6UrWtSrV68bijc1NbVTPic/P5+HH364U67t6xwOB3l5ec4pg3/961/069fPefLzqFGj5OaXj5HSFa3q06cPOTk5zptrU6ZMcftnFBQUyCY3X1JVlbNnz95wHE1sbCxms5nHH3+cLVu2EOnKUbRCd1K64pbi4+NvWNXgzg1pqqurqaioID4+3m3X9DXl5eU3PPllt9tJSUnhnnvuYfHixV87jkb4Nild0SaJiYlkZWWRlpaG0WjkG7c6ma+NCgoKGDBgQEDNQ9bV1XHo0CHnlMHFixe54447sFgsPPXUUyQnJ8vNLz8mpSvarF+/fuzbt4/09HSMRiMPPPCAy9cMhPW5TU1NzuNo9u/fT0FBASNGjMBsNrNq1SqGDRvmdRvKi84jpSvaZcCAAezZs4e5c+diMpmYOHGiS9fLz89n2LBh7gnnJRwOBydPnnROF3z66ackJSWRkpLCnDlzGDVqlFuOoxG+SUpXtNvgwYPZtWsX8+fPx2g0cvfdd3f4WgUFBfznf/6nG9Ppo7S01PlAwoEDB+jSpQtms5nHHnuM9evXd9pxNML3SOmKDhk2bBgvv/wyCxYsYN26dUyYMKHd17Db7RQVFfnkpuUVFRXOHbn2799PQ0MDZrOZ8ePHk5mZSe/evfWOKLyUlK7osBEjRrB9+3YWL17Mxo0bMZvNLb+4sRHy8yEvDyorQVW5bLNxR1AQ4T5wE62+vp4jR444pwzOnTvH2LFjSUlJYerUqfTv319ufok2kZMjhMuOHDnC0qVL2bJlC3fccce/v6Gq8Omn8OMfw+9/D0ajdjhVczMAtU1N1NbX0yMuDh5+GL73PRgzpmOHUrlZc3Mzn3/+uXPK4MSJEwwbNoyUlBQsFgvDhw+XDddFi+S4HtHpDh48yLJly3jppZcYPXo0XLoEy5fDRx9pB1NFRWmle52LZWUYDAa6d+0KVVXaiYD33gubNoGH16aqqkpRUZGzZA8fPkzfvn2dx9GMHj1ajqMRbSalKzxi//79rFy5kuwZMxi4bRvU1UF0dIsj15IzZ+gaE/PvJ6xUFa5ehfBwyMmBDswTt8eFCxdueCghNDQUi8VCSkoKKSkpdO3atVM/X/gvOSNNeITFYmHHt75F6Jw5NPTuTegtSquhoYGQ65dOKQrExGhl/b3vweuvgwsrI77q2nE010azVVVVmM1mUlJSSEtLo2/fvm77LCFaIqUr3KekhJG7d1Pbsyclly6RGBbW4nrUZrsd1eG4+THd4eFQXw9z5mhzwUlJHYrT2NjIp59+6izZ06dPM3r0aMxmM9/61rcYOHBgQD0JJ7yDlK5wD4cDFi4Em42IuDh6BQdTUlJCYlISoSEhX3t545ej3BZvmYWFaascFi2Cd97R5ntvwW63f+04mkGDBmGxWFi4cCEjR468eckL4UFSusI9fvc7OHpUm8MFoiIjQVUpKSkhKTGRkK8Ub0Nj403L+AbR0do1f/tb+H//72vfVlWVU6dOOfcwOHz4MD169MBisTB9+nTGjh0rx9EIryOlK9wjJwdMphtumkVFRWnndJWUkJSURMh1+742NDQQcavVAIqiXTMnx1m6ZWVlNxxHYzAYsFgsPPjgg6xYsYK4uLhO+eMJ4S5SusJ1BQXaQw83OeMsOjoaFSg5fZpeCQm8XF5Obl0dZbW19IuIYL7JxIQuXVq8tD0sjMajR/nvJUv4Y3Ex5eXljBs3DovFwrPPPivH0QifI6UrXHf0qLbcq4Xyi4mO1qYCSkroFhVFTkICV4uKuNyzJ8vOneMX/frR58tRsENVqa+ro7aujtraWhobG4k1GBhcX89d69czZMgQufklfJqUrnDdwYNa6baia0wMqqry4JUrRHfpQmNICPdGRtInKIijVVUEKwq1tbXU19cTEhJCREQEPXr0ICwsDENlJT0iIsDPdiMTgUlKV7ju9Glow6qA2C/X7Z49cwaTycS/SkrIu3qVcLud5shIYrt2JTw+HuNXR7JBQdpnCOEHpHSF6+z2Nu+XENu1K4qiUFldza6GBlJ79ODeWz2UoCjO/RqE8HUyOSZcFxmpFW8bRUdH80ODgfCQEJb36XPrN9jt2mcI4QekdIXrxo6FpqY2vVRVVdafP8+V5ma2xcdjassIuakJrt+9TAgfJtMLwnW33QbXrcFtzeYLFyi22chKTCSkrasQgoO1zxDCD0jpCtelpGjbNjY1tXpD7XxTE7+prCRYUfhGQYHz6yt69eLhlo6zaWrSrt3aBulC+BApXeG68HCYOhXeeANiY1t8We+gIA62d9lXdTU8/bT2GUL4AZnTFe4xc6Y2DWCzue+aNpt2zZkz3XdNIXQmpSvco29fWLkSamtv+aBEm6iqdq3nn4f4eNevJ4SXkNIV7jNtGkya5Dx4ssNUVbvGfffB9OnuyyeEF5DSFe5jMMDevXDPPVBR0bEHGpqbtffefbd2LdlnQfgZ+YkW7hUSAq++CvPmadMDlZXaBue34nBor62r0977wx9CC6dOCOHLpHSF+wUFwYIF8D//A3feqa1AKC/XTvy12bSCdTi05WBVVf/+3p13wrvvau+VEx6En5IlY6LzDBsGP/sZnDmjnf7wySdw7JhWsKDtv3vnnTB+PDzyCCQm6ptXCA+QI9iFEMLNWjuCXaYXhBDCg6R0hRDCg6R0hRDCg6R0hRDCg6R0hRDCg6R0hRDCg6R0hRDCg1pdp6soyiVAjmEVQoj2SVJVtfvNvtFq6QohhHAvmV4QQggPktIVQggPktIVQggPktIVQggPktIVQggP+v/CiVtxc1YEEgAAAABJRU5ErkJggg==\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -418,8 +442,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 11,
    "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T18:00:12.595984Z",
+     "start_time": "2019-12-12T17:59:52.126693Z"
+    },
     "scrolled": true
    },
    "outputs": [
@@ -427,16 +455,16 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "energy: -1.4999632680981052\n",
-      "time: 62.78843593597412\n",
-      "max-cut objective: -3.999963268098105\n",
-      "solution: [0. 1. 0. 1.]\n",
+      "energy: -1.4997714562104303\n",
+      "time: 20.23039197921753\n",
+      "max-cut objective: -3.9997714562104303\n",
+      "solution: [1. 0. 1. 0.]\n",
       "solution objective: 4.0\n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
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ZZ/D395fpmBogoauTCxcusHTpUn788UciIiJ47LHHpI0gdNezZ098fHyu3QRz5syZMiljZvJTXsuKiopYs2YNQ4YMoWnTpqSlpfHEE09I4Io6o02bNqxfvx6j0ShjZTVAftJriaqqfPzxxwwePJgDBw6QmJhIZGQkzs7OepcmxD84OTnx4osvMmjQIEaMGMFXX32ld0kWQ9oLtWD//v3ExcWRl5fH888/T9euXfUuSYibUhSFoKAgbrvtNmbMmMGePXtkrMwM5F+vBl2+fJkXXniBiRMn0r9/f5KSkiRwRb1zdaxs165dREZGkpmZqXdJ9ZqEbg0oLi5mw4YNBAUF4erqSmpqKoMGDZIVgqi3mjRpwrJly/D19SU0NJQ9e/boXVK9Je0FM1JVlS+++IIlS5bg4+PDunXraNOmjd5lCWEWNjY2TJgwAT8/P6Kjoxk7diwBAQEyVlZJErpmcujQIeLi4sjIyODZZ5/lvvvu07skIWpEjx498Pb2vjZWFhsbK2NllSDvd6spMzOT+fPnM378eHr37s3mzZslcIXFuzpWpqoqYWFhnDp1Su+S6g0J3SoqKSkhKSmJwMBAHB0dSUlJITAwEBsbG71LE6JWODk58cILL+Dv78/IkSNlrKyCpL1QSaqq8vXXX7NkyRJat27Nm2++Sdu2bfUuSwhdXB0r+/vVymTxUTZFVdUyH7znnnvUn3/+uRbLqduOHj1KXFwc58+fZ/LkyfzrX//SuyQh6ozMzExiY2MBmDdvHk2aNNG5Iv0oivKLqqr33OgxaS9UQHZ2NgsXLmTs2LE89NBDbNmyRQJXiL9p3LgxS5cu5fbbb5exsnJI6JajtLSUzZs3ExAQAEBKSgpDhgzB1la6MkLciI2NDZGRkUybNo3o6Gi2bdtGee+mrZGkRxm+/fZb4uPjadasGatXr8bb21vvkoSoNx5++OFrY2W7d+9m5syZcn3oP8hK92+OHz9OVFQUixYtIioqiqVLl0rgClEFrVu3Zv369QCMGDGCkydP6ltQHSGh+4ecnBzi4uIIDw/n/vvvJzk5me7du8vZNkJUg6OjI3PnzmXw4MGMGjWKL774Qu+SdGf17QWj0UhaWhpvvPEGPXr0YNu2bTRu3FjvsoSwGIqiMHjwYG677TamT5/O7t27iYiIsNqxMqte6f74448EBwfz6aefsmzZMmJjYyVwhaghnTt3JikpiX379hEZGUlGRobeJenCKkP35MmTTJ48mXnz5jFu3DhWrFhBhw4d9C5LCIt3dazMz8+PkJAQdu3apXdJtc6qQvfKlSssWbKEESNGcOedd7Jt2zZ69uwpfVshapHBYCAiIoIZM2YQExPD1q1brWqszCpC12QykZaWhr+/Pzk5OWzdupXhw4djb2+vd2lCWK3u3buzdu1a3n77bebMmUNBQYHeJdUKiw/dX375hZCQEN5//30SEhKYM2cOt9xyi95lCSHQxsrWrVuHwWAgLCzMKsbKdJ1eKCyE9HQoKQF7e2jVSvvVHM6cOcNrr73Gvn37mDhxIr1795Y2ghB1kKOjI88//zxpaWmMHDmSZ599lp49e+pdVo2p9dA9fhw2b4aPP4YTJ8DWFhQFVBWMRvD2hr59YcgQLYQrKz8/n7Vr15KWlkZISAgvvvgiDg4OZn8eQgjzURSFgICAv4yVjR8/3iLHymrtKmPp6RAbC//7nxawjo7g4ADX3zbMZNJWv0VFWhD36QNz54KHx833bzKZeO+991i+fDn3338/kZGReFTkC4UQdUpWVhaxsbGYTCbmz59fL69WVt5VxmoldFNTYdYsrY3g4vLXoC2LyQQ5OeDkBIsWaavfsvz2228sWrQIe3t7YmJiuP3226tdsxBCPyaTiZUrV/Lee++xYMEC7rjjDr1LqhRdQ3fpUoiPhwYNtJVtZRUWah/PPQehoX997OzZsyQkJLBr1y4mTJhA3759pW8rhAX56quvePHFFwkPDycoKKje/Hzrdj3dLVtg8WJtdVvVtqqjoxbYc+fChx9qf5afn8+KFSsYOnQo7dq1IzU1lX79+tWb/xAhRMV0796ddevW8e677zJr1qwqjZWpKmRmwpkzcO4cFBfXQKGVUGMr3RMntJaAvb15JhIKC0FRVGbO/JSkpHi6dOnChAkTaNasWfV3LoSo0woLC1mwYAF79+5l0aJFtGnTptztCwrggw/g7bdh9264cgVsbLQANpm0A/YPPQRPPw3t25u/Xl3aC089Bb/8Am5uVfryfygoyOfkyVxatNhDSopHvevxCCGqR1VV3n77bZYvX05sbCy9evX6xzYlJbBqFaxYoa1oFUU7LnR1Sgq00C0qurqQgy5dYN48MOeVAMoL3RoZGdu7VwtcV9fq76u0tITz5y+Qn59Hs2ZNMZl60aSJtBGEsDaKouDv789tt93GtGnT2LNnz1/Gyg4dgshIOHxYa0mWlT8GgxbETk7aynfHDhgwACZPhjFjKnagvzpqZPcbN2pP5mYtVpOpmPT0Fzh8+DEOHOjO0aPBXLnyLQCqauLixYscPXoUe3s7fHx8cHNzQ1UVNm+uiaqFEPVBp06dSEpK4sCBA0RERJCRkcHOnTBoEBw9qr27rmhLU1G0cHZygoULYdo0bSVck2okdD/5RHuluTkjdnbNadNmNR07foGHxzOcOTODjIz9HD58hOLiItq1a4eHR1MMBu3VzNER/vvfmqhaCFFfuLm58frrr3PXXXfh7x/D4MEFlJZqAVqV4+l2dlpYv/02zJ9v/nqvZ/bQzcjQjhTa2VXgLzc44eExBnv7liiKATu7eyguduXSpZ9p1aolrVp5Ymf315csBwc4dkzryQghrJfBYGDMmGcoLV3EmTMZFBdnAFW/WpnBoE1arV8P339vtjL/+feYe4fHj2uBW5lXm9LSEtLT0zl5cjeKch4vr3/j7HzjpbLBoDXFT582T71CiPorMRHOnbuF9u09yM7O4syZM5hMxirvz8ZGy69Jk2puYWf2A2lXT+GtjMOHD1NUlI+iLMFgeJBTp0qBwyiK1jzX5m+VPz6H0lIH5sxZS9Oml7CxscHW1vbar3//vKzHzLn9jbazsbGRuWEhalBpKSxbprUc7e3tadu2LWfPnuP48eN4enpib1+1kwMaNIDLl+Gjj+Dxx81cNDUQulWZyfX09OTYsanY2zvTrt08FMUGVeWPCxurqKqKNtmmfZ6XZ+CJJx6lRYsrlJaWYjQaKS0t/cvnN/qzoqKiSm1//cfft7vZ9iaTCYPBUCfCvzLbl/Vnf3/MYDDIi4rQ1VdfQXa21hIAUBQDLVu2ICsri+PHj9OiRQsaNdIeNBpzOHv2Ba5c+R5bWzc8PCJxde1X5r5tbLTRs3oRuq1ba7NyFZleAC1Yc3LiadjQiKJM5dy5i7Rq1RIbmxuXpqraavrxx7vg7Gzm4s1IVVWMRmO54VzZ8L/Z9oWFhdV+sShvu+v/zGQyVSicazr8a2r7P99hibrqk0+01e5fKbi5NcbR0ZHTp09TUFBA06ZNOXduAYpiR8eOH1FYeJBTpybi6NgRBwfvG+67QQPYt08LdXOMvl7P7KHr4QENG2r/GBU5mHbu3MsUFx/Dy2s5BoMTFy5c4NixY3h6euLo6PSP7YuKwNOTOh24oLVFrv4AW+KlJU0m019eVMz9TuFG2xUUFFT7xaKi25tMpnr5YlHedtc/ZqjpYdRa8PPP2qjXjTg6OtGuXTvOnEnn+PEDFBR8io/PNgwGZ5yd76JRo4fJzv5/NG064YZfryhafu3bB926mbdus4euosDDD8N778HNbqxbUnKWrKw0FMWeQ4f+vIyYq+skTp400bRpU9zc3IA/VxwFBdC7t7mrFpVlMBgwGAzYVeSVtR4ymUw1Eublbf/39ld529/shay87UtLS68tCvQO/6q20mxsbDh0qBmNGmnX4f7zuM+fn9vY2NKmTWvS078nK6uE0lL3a+1PB4cO5Of/Wu73QEmJNvdb50MXYPhweP/9m7cY7Oxa4Ot749OMmzQp4vTp0+TnF9CiRXMUxYCqatMLQ4fWRNVC/MlgMFj0PfSuvqjU1DuFv29fUlLyj3cqVX1x0T5MnDu3mkuXslGUP4/7XD0OdH0AG43nUFVHjhw5jJ+fH6BgMDTEZMq7yb+RdqqwudVI6N59t3Ye86FDVe+H2Ns7/HE08uy1o5F5efbcfz/4+Ji3XiGszdUXlfr8wuLtDY0bN7vBwk6b1VVVlaKiYtLT0ykuLqR16zZcfddsMuVhMJR/BtfVFoO51UhjR1EgLk77/J+N7oozGGxo1aoVbm5uHDlympKSfF5+2Tw1CiHqt5Yty5qlVTCZtMsInDx5gkaNOuDgYI+zc861LYqKDpZ5EO0qe3vt+JG51Vg33ddXGzDOza3uucwKLi5NcHNrRePGCbz33nJMNX1ytBCizrv77hu9/VfJysrkyJEjGI1GvL298fBojYtLLy5eXInJVEB+/k5yc7/E1XVAmftWVW3B6Otr/rpr9BBmRAQEB2tjF1Vd8RYXa7ftmTzZgU8/HcPOnTuZMGECWVlZ5i1WCFGv9Oz512NG+fl5HDt2jKysbFq3bk2LFi2xtdX6A82bz8BkKuLgwUc4cyaW5s1nlrvSLSzUJrFq4nLdNX67HlXVzhpJSNB+36hRRed3tbC2sYFnn9Vu1aMoYDQaWbZsGR999BGvvPKK3A9NCCuVnw/33AOKUszlyxcoLNRmcl1cXLh+4qkqsrK03Bkxompfr9vterS/XLvG5TvvaAfAcnK0J3T1BIrrqar255mZ2nZ33KFd/X3YsD+D2sbGhqioKGJiYpg0aRKpqamU98IhhLBU+fj4fMfRoxdxdHTAx8cHFxdXqhu4RUXahbUGDjRPlX9XaxPSnTppY2RbtsCjj/65ks3L+/MjO1sbCRs4ENLStLsIe5fxDqBnz56sWbOGrVu38vzzz1NYE7MdQog6x2QysX37dgICAujU6VNuu60pTk4eKEr140xVtRX0nDk3P8+gqmrlFuw3oqpw/jycPKmtbu3twctL66NU5uzLgoIC5s+fz+HDh1m4cCGtW7eukXqFEPrbuXMncXFxGAwGYmJi8PPz47vvtHfDTk7Vux/j1YVgt27ajRiqc9Kerrdgrw2qqrJt2zbeeOMNZs+eTffu3fUuSQhhRufOnSMhIYHffvuNyMhI+vXr95dTmdPSYOpULXgdHSu/f1XV2p6dOsHmzdqlDKpD155ubVAUhaCgIOLj41mwYAHLli2TsTIhLEBBQQGrVq0iODiYNm3akJqaSv/+/f9x7Qh/f+2A/dXwrMxhnsJCbYXbs6d5AvdmLCJ0r/Lz8yMpKYk9e/YQGRlJZmam3iUJIapAVVU++OADAgICOHHiBJs2bWLcuHE4lXWFG6BfP/j0U3jwQS1EMzO11uWNmEzabdmvHkdavBjeeKPmAxcspL3wd0ajkRUrVvDBBx+wYMGCP863FkLUB7///juLFi2itLSUmJgY7rrrrkrvY/9+rS/73nvagTFb2z+vBXP9iQ+jR0OfPmVfrayqLL6nW5Yvv/ySl156idGjRxMYGCjXRxWiDrtw4QJLly7lp59+IiIiggEDBlT7EpSqCufOabdlvxq+zZpp14apySuulhe6NXLBm7ri4Ycfxtvbm2nTprFnzx5mzpxZ7tsTIUTtKyoqIikpibfeegt/f39SU1NxNtMFsxUFWrTQPuoKi+rp3kjr1q1Zt24dAGFhYZw8eVLnioQQoPVtP/74YwYPHsyBAwdITExk/PjxZgvcusqi2wvXU1WV1NRUVq1axbPPPkuPHj30LkkIq7V//37i4uLIy8sjJiaGrl276l2SWVlte+F6iqIwePBgbrvtNqZPn87u3buJiIjAxsZG79KEsBqXL19m2bJl/O9//2PcuHEMHDjQIm4dVBnW9WyBzp07k5SUxL59+xg/fjwZGRl6lySExSsuLmbDhg0EBQXh6upKWloa/v7+Vhe4YIWhC9C4cWOWLl3KHXfcQUhICLt27dK7JCEskqqqfP755wQGBrJz507WrVvHxIkTaVgbA7F1lNW0F/7OYDAQERFB586diYmJITw8nKCgIBkrE8JMDh06RFxcHBkZGcTGxnL//eDxd9sAABDmSURBVPfrXVKdYDUH0spz+vRppk2bRrt27Zg1a5aMlQlRDZmZmaxYsYIvvviC0aNH4+/vb3XHTiz+2gvV5enpybp167Czs2P48OEyViZEFZSUlLBp0yYCAwNxcHAgJSWFwMBAqwvcm5HQ/YODgwPPPfccQ4YMYdSoUXz22Wd6lyREvaCqKl9//TVPPfUUP/zwA2+++SYxMTF/3MFB/J3V9nRvRFEU/P39/zJWFhkZKa/UQpTh6NGjxMfHc/bsWaZMmcK//vUvvUuq82SlewOdOnUiMTGRQ4cO8cwzz3D58mW9SxKiTsnOzmbhwoWMGTOGf//73yQnJ0vgVpCEbhnc3NxISEiga9euhIaGsnPnTr1LEkJ3paWlJCcnM3jwYFRVJSUlhaeffhpbW3nTXFHyL1UOg8HA2LFj6dy5M1OnTmXEiBEMGTJExsqEVfruu++Ij4/Hw8ODlStX4uPjo3dJ9ZKMjFVQeno6U6dOxcvLi1mzZln8RTmEuOrEiRMsXryYEydOEB0dzUMPPSQLj5uQkTEzaNmyJWvXrsXR0ZHhw4dz/PhxvUsSokbl5uYSHx/PyJEj6dq1K8nJyXTv3l0Ct5okdCvBwcGBOXPmMHToUMLDw/n000/1LkkIszMajaSmphIQEEBBQQHbtm0jNDQU++rcaldcIz3dKhg4cCC33nor06dPZ9euXUyYMEEOJAiL8NNPPxEXF4eLiwtLly6lY8eOepdkcaSnWw3Z2dnMnj2bgoICXn75Zdzd3fUuSYgqOXXqFK+99hoHDx5k0qRJ9OzZU9oI1SA93Rri6urKkiVLuPfeewkNDWXHjh16lyREpeTl5ZGQkEBYWBi33347KSkp9OrVSwK3Bsl74moyGAyMGTOGzp07M336dIYPH05wcLB804o6zWQysX37dlasWMEDDzxAcnKyvFOrJdJeMKP09HSmT59Oq1atmDNnjoyViTppx44dLFq0CEdHR2JiYujUqZPeJVkcaS/UkpYtW7JmzRoaNmzIsGHDOHr0qN4lCXFNeno6M2bMYPbs2QwfPpw333xTAlcHErpmZm9vz6xZsxg2bBhjxozho48+0rskYeXy8/NZvnw5ISEh+Pj4kJKSQp8+faQFphPp6daQJ554gltvvZWpU6eye/duJk6cKGNlolaZTCbef/99li1bxj333MOWLVto2rSp3mVZPenp1rCcnBxmz57NlStXWLBgAR4eHnqXJKzArl27WLRoEYqiMGXKFPz8/PQuyapIT1dHLi4uLF68mAceeIDQ0FB+/fVXvUsSFuz8+fPMmjWLGTNmMGTIENatWyeBW8dI6NYCg8FAeHg4zz33HDNmzCAxMZHy3mEIUVmFhYWsXr2ap59+mlatWpGSkkL//v2t8hbndZ38j9SiBx54gA0bNvDxxx8zffp08vLy9C5J1HOqqvLf//6XgIAAjh07xqZNm3jmmWdkXLEOk9CtZS1atODNN9/Ezc2N0NBQGSsTVbZ3715GjRpFYmIiL730Ei+//DItWrTQuyxxE3I4XQf29vbExsayfft2xowZw9SpU+nbt6/eZYl64uLFiyxdupQffviBiIgIHnvsMWkj1CMSujp6/PHHr42V7dq1i0mTJmFnZ6d3WaKOKioqYtOmTWzatIlBgwaRlpYmbYR6SF4eddaxY0cSExNJT09n7NixXLhwQe+SRB2jqiqffPIJgYGB7Nu3j40bNxIZGSmBW0/JnG4dYTKZWL9+PVu3buWll17inntuOOInrMyBAweIi4sjNzeXmJgY+b6oJ8qb05X2Qh1hMBgYOXIknTt3JjY2lqFDhzJs2DA5VdNKZWRksHz5cr766ivGjRvHwIEDpW9rIeR/sY6577772LhxI5999hlTp07lypUrepckalFxcTEbN24kMDCQBg0akJqair+/vwSuBZH/yTqoefPmvPHGG7i7uzNs2DAOHz6sd0mihqmqyhdffEFQUBC//fYb69atIzo6mkaNGuldmjAzaS/UUfb29syYMYP333+fcePGERMTw6OPPqp3WaIGHD58mPj4eC5dusSMGTPo1q2b3iWJGiShW8f179+fDh06MG3aNHbv3k10dLSMlVmIzMxMVq5cyeeff054eDgBAQHY2NjoXZaoYdJeqAc6dOjAxo0bOX/+PKNHj5axsnqupKSEt956i8DAQOzs7EhJSSEoKEgC10pI6NYTjRo14tVXX6VHjx6Ehoby448/6l2SqIJvvvmGp556iu+++4433niDKVOm4OLiondZohZJe6EeMRgMhIWF0alTJ2bPns2QIUMYPny4HNmuB44dO8bixYs5ffo0kydP5t///reMA1op+Wmth+677z4SExP56quvmDJlCrm5uXqXJMqQk5PDq6++yujRo+nWrRvJyck8+OCDErhWTEK3nmratCmrV6+mefPmDBs2jEOHDuldkriO0Whk69atBAQEYDQaSUlJITg4WA6CCmkv1Gd2dnZMmzaNDz74gGeeeYbo6GgGDBigd1lW7/vvvyc+Ph53d3dWrFhB+/bt9S5J1CESuhbg0UcfvTZWtmvXLmJiYrC3t9e7LKtz8uRJFi9ezLFjx4iOjqZ79+7SRhD/IO0FC9G+fXs2btxIRkYGo0eP5ty5c3qXZDVyc3NZvHgxI0aM4O6772br1q08/PDDErjihiR0LUjDhg1ZuHAhvXv3ZtiwYfzwww96l2TRTCYTaWlpBAQEkJeXx7Zt2xg2bJi8yxDlkvaChVEUhWHDhtGpUyeeffZZnnrqKcLCwmSszMx+/vln4uLiaNSoEa+//jq33nqr3iWJekKup2vBLly4wIwZM3B1dWXu3LkyhG8GZ86cYcmSJRw4cICJEyfSq1cvaSOIfyjverqy/LFgTZs2ZdWqVbRq1YrQ0FAOHjyod0n1Vn5+Pq+//jrDhg3D19eXlJQUevfuLYErKk1C18LZ2dkxZcoUIiIiiIiIYPv27XqXVK+YTCb+85//4O/vz+XLl9myZQsjR46Uvq2oMunpWom+ffvSoUMHpkyZwu7du5kyZYoEx03s2LGDuLg47O3tiY+Pp1OnTnqXJCyArHStiLe3N4mJiWRlZREeHs7Zs2f1LqlOOnv2LDNnzmTWrFmEhoayZs0aCVxhNhK6VqZBgwa88sor9OnTh+HDh/Pdd9/pXVKdkZ+fz4oVKxg6dCjt2rUjNTWVvn37St9WmJW0F6yQoiiEhIRcGyvz9/dn1KhRVjtWZjKZ+PDDD1m6dCldunRh8+bNNGvWTO+yhIWSkTErd/HiRWbMmEHDhg158cUXrW6sbPfu3cTFxWEymZgyZQp33HGH3iUJCyAjY6JMHh4erFq1Ci8vL0JCQti/f7/eJdWKCxcuMHv2bKZPn05gYCDr16+XwBW1QkJXYGtry+TJk5kwYQKRkZG8++67epdUYwoLC3njjTcYMmQILVq0ICUlhQEDBlhta0XUPunpimseeeQRfHx8rl2tbPr06RYzVqaqKh9//DEJCQl07tyZpKQkWrZsqXdZwgrJy7v4C29vbzZu3EheXh4jR44kPT1d75Kqbe/evYSHh7NhwwZeeOEFFixYIIErdCOhK/7B2dmZl19+mf79+xMWFsa3336rd0lVcunSJebOnUt0dDRPPPEEiYmJdOnSRe+yhJWT9oK4IUVRCA4OxtfXl9jYWAYNGkR4eHi96H0WFxezadMmkpKSePLJJ0lLS6NBgwZ6lyUEICtdcRN33303iYmJ/PTTT0yaNIns7Gy9SyqTqqp89tlnDB48mN9//50NGzYQFRUlgSvqFAldcVNX7/Xl7e1NSEgIe/furdqOjEbIzITLl6GgwKw1Hjx4kLFjx7J69Wpmz57NokWL8PT0NOvfIYQ5SHtBVIitrS2TJk3Cz8+PqKgoxo8fz8CBA29+iuzhw7BtG/zvf3DoEJhMoChaALdoAV26wKBB8PDDYGNT6boyMjJYsWIFX375JWPHjmXgwIHYVGE/QtQWOSNNVNrx48eZOnUqfn5+TJ8+HQcHh39utH8/zJkDv/4KqgoODuDoCFd7wqoKxcXaitdgAFdXmDoVBg/+c5tylJSUsGXLFtavX8+AAQMYPXo0jRo1MvMzFaJq5Iw0YVZt27Zlw4YNFBYWMnLkSM6cOfPng0YjJCTA44/Djh1amDZuDM7Ofw1TRdGC2M0NXFy08J05E4YOhXKufqaqKl999RVBQUH88ssvrF27lsmTJ0vginpDVrqiylRVJTk5mbVr1zJnzhwe7NYNJk2CDz6ARo3AtpLdK1WFrCy45RbYuhXatv3Lw0eOHCEuLo6LFy8yefJkHnjgAfM9GSHMqLyVroSuqLadO3cyc+ZM5pWWctfhwyhubhVqEZQpOxuaNIH/9//A3Z2srCxWrVrFJ598wujRo/H398e2soEuRC2S9oKoUXfeeSdbwsNp9f33nMrNpbScF/IKcXWFy5cxzZjB5rfeIjAwEIPBQGpqKkFBQRK4ol6T715RfTk5uLz4Io1at+ZCbi7Hjh3D09MTJ0fHKu/yio0NVzZv5vKFC6xatQpvb28zFiyEfiR0RfX95z+Qm4vi5kYzJyecnJw4efIkTZs2xc3Njcrcd6GouJjz589TXFxMC3d3xhcVobRrV2OlC1HbJHRF9ZhMsGoVXHc1MpdGjXBwcOD06dMU5OfTvEULDIrC1owMtmdnc7ioiL4uLjx/3UVnjEYjFy9dIjs7G3d3dzw9PbXe19GjsGcP+PnV/nMTogZIT1dUz6lTcP48ODn95Y8d7O1p17Ytqqpy/PhxiouLcbe1ZZS7O0+4ul7bTgUyMjM5cuQIqsmEj48PtzRpgkFRtLGy0lL48staflJC1BwJXVE9+/Zp4XiDM9MMBgMtW7XCzc2N48ePc6/BQI9GjXD944yxK3l5HD16lNzcXNp4edGiRQts/342mb09fP99bTwTIWqFtBdE9Rw6pJ1ZVgYFaNK4MY6Ojpw5fZoCNzdKjUays7M5l59Ps2bNaNioUdl9XwcHOHCgJioXQhey0hXVk5dXoc2cnZxo164dBfn5ZGRkYGdnh7ePD43KC1zQ5n0LC81SqhB1gYSuqB57e+1MsgqwtbWljZcXHh4eODs7a33bm1HVKl0IR4i6SkJXVE+bNmBnV+HNlT8+Kqy4WPs7hLAQErqienx9K7wSNaoqxSYTJsAEFJtMGG+2Si4shPvuq3aZQtQVciBNVE+HDlrolpTcdMW75tIlVl+6dO3372dnM8bdnTEeHmV/kZ0dyIVthAWR0BXVY28PwcGwdq12CcdyjPHwKD9g/66oCBo0gO7dq1mkEHWHtBdE9YWEaKvd0lLz7jc/H8LDK9UzFqKuk9AV1eflBc88A7m5FZ5kuKmcHPD0hFGjzLM/IeoICV1hHuPHQ/v2WlhWV1GR9mtCgnaLHyEsiISuMA97e0hMhGbNtLs/VHXFW1CgTSwsWQJ33mneGoWoAyR0hfk0bQppaXD77VrwlnN68D+oqnZ7doNBu2pZ//41V6cQOpLQFeZ1NXhjY7XQzcrSVq9lrXxLS7Wwzc6GHj3g00+hV69aLVmI2iQjY8L8bGy0A2D+/vDOO9o4WXq61oIwGrVtDAbtWrx2dvDUUxAaqp1oIYSFk9AVNadxYxgxQvvIzob9++HiRS1sGzbUTqxo1ap6N7EUop6R0BW1w9UV7r9f7yqE0J0sMYQQohYpajmjPYqiXARO1F45QghhEbxUVb3hOe/lhq4QQgjzkvaCEELUIgldIYSoRRK6QghRiyR0hRCiFknoCiFELfr/QQEI+LmeO00AAAAASUVORK5CYII=\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -452,7 +480,7 @@
     "ry = RY(qubitOp.num_qubits, depth=5, entanglement='linear')\n",
     "vqe = VQE(qubitOp, ry, spsa)\n",
     "\n",
-    "backend = BasicAer.get_backend('statevector_simulator')\n",
+    "backend = Aer.get_backend('statevector_simulator')\n",
     "quantum_instance = QuantumInstance(backend, seed_simulator=seed, seed_transpiler=seed)\n",
     "\n",
     "result = vqe.run(quantum_instance)\n",
@@ -497,23 +525,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
+   "execution_count": 12,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T18:00:40.238107Z",
+     "start_time": "2019-12-12T18:00:12.618707Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
       "energy: -1.5\n",
-      "time: 72.55826902389526\n",
+      "time: 27.385574102401733\n",
       "max-cut objective: -4.0\n",
-      "solution: [1 0 1 0]\n",
+      "solution: [0 1 0 1]\n",
       "solution objective: 4.0\n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -563,8 +596,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
+   "execution_count": 13,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T18:00:40.513151Z",
+     "start_time": "2019-12-12T18:00:40.259077Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -578,7 +616,7 @@
     },
     {
      "data": {
-      "image/png": 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un/6kLfsKD7/1a8+dW0VdXS4ORz0mUzfi4mYQE/NYi68PDbXx2msN1NZu5fDhT4iOjsZisfDtb3+bzZs3ExkZ6Up04ee8fT73ZiZNmkRzczNz584lKyur0066EPpyqXT//ndtRNoW3bp9n6CgVRgMwTQ2nuL06dmEhAwhLEybw7Lb7dTV1VJTU0ttbS0Oh4OgoK4kJk5i0aK59OrVy5WoIsDk5+f75Ghx8uTJ2O12MjIyyM7Opl+/fnpHEm7mUukeOtT2hxlCQvpf92/aAt7q6pNUV8dRW1uLzdZIeHg4ERERxMZ2JSQkhKtXFXr3noT0rWiv/Px8nz254eGHH8Zut5Oenk5OTg5J3vK4pnALl0r34kVt1UJbnTu3iYqKd7HbG4AkgoIG0KUL9OzZg7CwMBTlxptfTU1w/rwrCUWgKigo8MmR7jWPPvooDoeDtLQ0XnnlFRISEvSOJNzEpdJtbm7fMrGIiDSqqh6jZ89y4ATdug1GUVqP0NTkSkIRiKqrq7l69Srx8fF6R3HJlClTaG5udhavHOHkH1xaVxUR0fY5XYCYmBgiI6NoaEiiubmMiopftfp6RdGWjwnRHgUFBQwYMMAvlg2mpqby9NNPY7VaKS0t1TuOcAOXfiqHDoWGhva8Q6Fv374oCly9WoHNdqbVV4eEgA//DVHoxNenFr7q8ccfZ/r06VitVi5evKh3HOEil0rXYgGb7dava24up6rqAxyOOkAlOroEm+0j6uv7ATff5UxVtemLoUNdSSgCUV5ens8tF7uVqVOn8p3vfIc5c+ZQVlamdxzhApdK98EHtQcZWtkd8ksKFRW/oqDgm+TnT+TSpd0kJCzDaDRz7lwpNyvemhqtcHv0cCWhCETevNGNK6ZPn05qaipWq5XLly/rHUd0kEs30oYN04qxoEDb7KbFDzF1JSnp1a99PSbGwZkzZygtLf1y39p/7wXpcIDV6ko6EYjsdjtFRUUMHDhQ7yidYsaMGTQ3N2O1Wnn11VeJjY3VO5JoJ5fvNCxdqk0DtOeG2jWKYiAhIYGmpmbOnz/PtRFvdTX06qWNpIVoj9OnT9OjRw/C2/KYpI965plnmDx5MlarlfLycr3jiHZyuXTvuQemTNE2vekIrXjjsdlsXLhwgeZmFbtd20gnJMTVdCLQ+NtNtJbMmjWLiRMnkp6eTmVlpd5xRDu4ZU3NunWQkNDx4jUYjCQkJFBX18iZM1fJzFQZM8YdyUSg8cU9FzpCURTS0tL4j//4D9LT06mqqtI7kmgjt5RuVBS8/Tb07QsVFR2barDZjERHJ5KU9Aeam1+mtbPbhGiJv95EuxlFUZg7dy5ms5mMjAyqq6v1jiTawG2rx3v2hPfeg0cf1Ua8NTVtWdWgnY9WUaH988svG/jrX7/J4cOH2Lt3rxSvaLe8vLyAmF64RlEU5s+fz+jRo5k7dy41NTV6RxK34NZHdqKjYdcu+OEPtSPVq6rgyhWtgJubtRJ2OKCxUSvmykrtNODHHoO//EX7PTo6iqysLP7+97+Tk5PjznjCz5WXl2Oz2QJuRzpFUVi0aBHDhw8nMzOT2tpavSOJVnTKc5KTJsEf/gC//jWkpcFtt2mlW1GhFXFEBDzwgDYXvH8/bN+ujZSviY6OJisri7/+9a+89tprnRFR+KFrN9GUWx1D7YcURWHJkiUMGjSIefPmUVdXp3ck0YJOO5taUeD227VfHREbG0t2djazZ8/GZDLxve99z635hP8JlJtoLTEYDCxbtowNGzawYMECdu3aRVhHT3cVncardwSJi4sjJyeH9957jzfffFPvOMLLBdJNtJYYDAZWrlxJnz59WLRoEQ3t2xxFeIBXly5A9+7dycnJ4Z133uEXv/iF3nGEF/PV0yLczWAwsHr1arp168bixYuxtWWDFOExXl+6AD179iQnJ4ef//zn/OpXrW8HKQKTzWajpKREzhX7ksFgYO3atURHR/Pcc89J8XoRnyhdgN69e5Odnc2Pf/xj3n33Xb3jCC9TXFxMfHy8nAp9HaPRyPr16wkNDWXp0qU0yYkAXsFnShegb9++ZGdn8+qrr/L+++/rHUd4kUC/idYSo9HIpk2bMJlMLFu2jObmZr0jBTyfKl2AxMREsrKy2LdvH7///e/1jiO8hNxEa5nJZGLz5s2oqsqKFSukeHXmc6ULkJyczL59+9i5cyd/+tOf9I4jvIDcRGtdUFAQW7ZsoaGhgdWrV2O32/WOFLB8snQB+vfvz969e9m2bRv/93//p3ccoSNVVWV6oQ2Cg4N56aWXuHr1KmvWrMHRkU1ShMt8tnQBBg0axJ49e9iyZQsff/yx3nGETi5duoTJZCIuLk7vKF4vODiYHTt2cOXKFdatWyfFqwOfLl2AIUOGsHPnTtavX8/f//53veMIHQTaJjeuCgkJYceOHZw/f54NGzZI8XqYz5cuwPDhw9mxYwdr167lk08+0TuO8DC5idZ+YWFh7Ny5k5KSErZs2SLF60F+UboAI0eOZNu2baxcuZIDBw7oHUd4kMzndkx4eDi7d+/m5MmTbNu2TbZS9RC/KV2A0aNHs3XrVpYvX87hw4f1jiM8REq3464V7+eff8727duleD3Ar0oXYOzYsWzatIkf/OAHHDt2TO84opPV19dz8eJFkpKS9I7is7p06cLevXs5evQoO3fulOLtZH5XugBms5kXXniBxYsXc/z4cb3jiE5UWFhIcnIyJlOn7VIaECIjI9m3bx8HDhxgz549UrydyC9LF+DOO+9kzZo1LFy4kBMnTugdR3QSmVpwn6ioKLKzs/nnP/9Jdna2FG8n8dvSBbjrrrt4/vnnmT9/Pvn5+XrHEZ1AnkRzr2untnz44Yf88Ic/1DuOX/Lr0gW49957WbZsGZmZmRQWFuodR7hZfn4+Q4YM0TuGX+natSs5OTl88MEHvP7663rH8Tt+X7oAkyZNYvHixWRkZFBcXKx3HOEmDoeDkydPyki3E8TGxpKTk8Nvf/tbfvKTn+gdx68EROkCTJ48mXnz5pGens7p06f1jiPcoLS0lMjISKKiovSO4pe6detGTk4O7777rhyX5UYBU7oA3/zmN0lLSyMtLY0zZ87oHUe4SG6idb4ePXo4j8t666239I7jFwKqdAGmTJnCs88+S1paGqWlpXrHES64duS66FzXjst66623+OUvf6l3HJ8XcKULkJqayowZM7BarVy4cEHvOKKD8vLyZKTrIdeOy/rpT3/Kb37zG73j+LSALF2AJ554gqlTp2K1WikrK9M7jugA2ejGs64dl/Xaa6/x3nvv6R3HZwVs6QJMmzaN1NRUrFYrly9f1juOaIfq6moqKyuJj4/XO0pASUhIICcnh1deeUXOKeyggC5dgBkzZvDoo49itVopLy/XO45oo4KCAgYOHIjBEPA/wh537ZzCvXv3yjmFHSA/scAzzzzD5MmTsVqtVFRU6B1HtIHcRNNXcnIyWVlZ7Ny5kw8++EDvOD5FSvdLs2bNYuLEiWRkZFBVVaV3HHELslxMf9fOKXzppZf4y1/+onccnyGl+yVFUUhLS2P8+PGkp6dTXV2tdyTRCild7zBo0CD27t3Liy++yIcffqh3HJ8gpXsdRVHIzMxkzJgxzJ07l5qaGr0jiZuw2+0UFRUxcOBAvaMIYPDgwezatYtNmzbJAbFtIKX7FYqisGjRIoYPH868efOoq6vTO5L4itOnT9O9e3fCw8P1jiK+NGzYMF5++WXWr1/PP/7xD73jeDUp3ZtQFIUlS5YwYMAA5s+fT319vd6RxHVkfa53GjFiBNu3b2fNmjVyQGwrpHRbYDAYWL58OQkJCSxcuJCGhga9I4kvyXyu97r99tudB8Tm5ubqHccrSem2wmAwsHLlSnr06MHixYux2Wx6RxLISNfbXTsgdsWKFRw6dEjvOF5HSvcWDAYDa9asITo6mueee06K1wvIaRHeb+zYsWzevJkf/OAHHDlyRO84XkVKtw2MRiPr168nNDSUZcuW0dTUpHekgFVeXk5jYyO9evXSO4q4hZSUFDZu3MjSpUvlZO7rSOm2kdFoZNOmTSiKwooVK2hubtY7UkC69iSaoih6RxFtYLFYWLduHYsXL+azzz7TO45XkNJtB5PJxObNm7HZbKxatQq73a53pIAjN9F8z4QJE1i9ejULFy7k888/1zuO7qR02yk4OJht27ZRXV3NmjVrcDgcekcKKHITzTfdfffdrFy5kgULFpCXl6d3HF1J6XZAcHAw27dv58qVK7zwwgtSvB4kN9F81/Unc+fn5+sdRzdSuh0UEhLCjh07KC0tZdOmTVK8HmCz2SgpKWHAgAF6RxEdNGnSJJYsWUJmZiaFhYV6x9GFlK4LwsLC2LlzJ8XFxWzduhVVVfWO5NdOnTpFfHw8wcHBekcRLnjwwQdZuHAhGRkZFBcX6x3H46R0XRQeHs7u3bv54osv2L59uxRvJ8rLy5OpBT/x0EMPkZmZSXp6OqdPn9Y7jkdJ6bpBREQEe/bs4dixY+zatUuKt5PITTT/8sgjj5CWlkZaWhpnzpzRO47HSOm6SWRkJHv37iU3N5d9+/ZJ8XYCWS7mf6ZMmcKzzz6L1Wrl3LlzesfxCCldN4qKiiIrK4u//e1vvPrqq3rH8Suqqkrp+qnU1FS+//3vY7VaKS0t1TtOp5PSdbOYmBiysrL4y1/+wuuvv653HL9x6dIlTCYTcXFxekcRneDb3/42Tz75JFarlQsXLugdp1NJ6XaC2NhYsrOz+d3vfsdPfvITveP4BVmf6/++853vMHXqVKxWK2VlZXrH6TRSup0kLi6O7Oxs3n33Xf77v/9b7zg+T6YWAsO0adNITU3FarVy6dIlveN0CindTtSjRw9ycnJ4++23efvtt/WO49NkpBs4ZsyYwaOPPkpaWhpXrlzRO47bSel2sp49e5KTk8Obb77Jr3/9a73j+Kz8/HyGDBmidwzhIc888wzf+MY3SEtLo7y8XO84biWl6wF9+vQhOzubH/3oR7z33nt6x/E59fX1XLx4kaSkJL2jCA+aNWsW9913H+np6VRWVuodx22kdD0kPj6e7OxsXnnlFd5//3294/iUwsJCkpOTMZlMekcRHma1WrnrrrtIT0+nqqpK7zhuIaXrQYmJiWRlZbF3717+8Ic/6B3HZ8hNtMClKAoZGRmYzWa/KV4pXQ9LTk4mKyuLl19+mT//+c96x/EJ106LEIFJURTmz5/PmDFjmDt3LjU1NXpHcomUrg769+/Pnj172Lp1K3/961/1juP18vLyZKQb4BRFYdGiRdx2221kZmZSW1urd6QOk9LVyeDBg9m9ezebN2/m448/1juO13I4HJw8eVJKV6AoCkuWLGHQoEHMmzePuro6vSN1iJSujoYOHcrOnTtZv349//jHP/SO45VKS0uJjIwkKipK7yjCCyiKwrJly0hOTmbBggXU19frHandpHR1Nnz4cLZv386aNWvYv3+/3nG8jtxEE19lMBh4/vnn6dOnDwsXLqShoUHvSO0ipesFbr/9drZu3crzzz/PwYMH9Y7jVeQmmrgZg8HA6tWr6d69O4sWLaKxsVHvSG0mpeslxowZw4svvsjy5cs5cuSI3nG8hox0RUsMBgNr164lJiaGJUuWYLPZ9I7UJlK6XuSOO+5g48aNLF26lGPHjukdxytI6YrWGI1G1q9fT1hYGEuXLvWJ4pXS9TJms5l169axePFijh8/rnccXVVXV1NZWUl8fLzeUYQXMxqNbNy4kaCgIJYvX05TU5PekVolpeuFJkyYwOrVq1m4cCFffPGF3nF0U1BQwIABAzAY5MdUtM5kMrFp0yZUVeX555+nubm5xdfW1cGhQ/Dmm7BmDSxbBi+8AO+8A8ePQ2d3ttLaWV7jxo1T5caOfj788EM2bdrE3r17A/Kv2G+//TaFhYWsWLFC7yjCR9hsNpYsWUJ4eDgbNmzAaDQ6v1dYCG+8Ab/6FaiqVq6qCoqi/W40ar9CQ+GZZ2DqVOjRo2M5FEU5pKrquJt9T4YQXmzixIksXbqUzMxMCgsL9Y7jcTKfK9orODiYbdu2UV1dzerVq3E4HDQ2wrZt8PDD8POfQ0gIdOkCXbtCbOy/f4+O1r5ut8Pu3dCHwrUAABFRSURBVHDfffDLX4LD4d6MUrpe7oEHHmDhwoVkZGRQXFysdxyPkiPXRUcEBwezfft2KioqeO65bTz6qMorr0B4uFawt9qsLiQEYmLAYIDly+HZZ8Gdz2DIXnk+4KGHHsJut5ORkUFOTg6JiYl6R+p0drudoqIiBg4cqHcU4YNCQkJYvnwHZvMZbLZy+vWLBZR2XgOCg+Gjj7Tphjfe0L7mKhnp+ohHHnkEq9VKWloaZ8+e1TtOpzt9+jTdunUjPDxc7yjCBzkcsGBBKGFhAzAYqjl//gLQ8v2rliiKNurdvx82bXJPNildHzJlyhRmzpxJWloapaWlesfpVDK1IFzx05/Cp59C164GEhISaGxs/PJo944Vb3S0ttohN9f1bFK6PiY1NZWnnnoKq9XKxYsX9Y7TaeQmmuio6mp48UWIiNAK02AwkpiYQENDAxcuXKQjxWs0anPBK1ZoKx1cIaXrg5544gmmTp2K1WqlrKxM7zidQka6oqP+53+05WDBwf/+mla8ic7z9kClvPyXFBc/xRdf3Elp6dpbXrdLFzh9WhtBu0JK10dNmzaN//qv/8JqtXL58mW947idHLkuOuonP7mxcK+5Vrx1dXWUlZVhMsXRrdtMoqOntOm6iqLNFb/1lmv5pHR92IwZM3jkkUf87pjqiooKGhsb6dWrl95RhI+pr9cegggLu/n3jUateGtqamlsHElk5L0YjdFtvn5YGHzyiWsZpXR93MyZM3nggQf86pjqa9s5Kkr7lvgIkZ8PQUHaqLQlRqOJpKREqquruXSpfX9LDAmBc+e0R4k7SkrXD8yePZu7777bb05LzcvLk6kF0SFXrrReuNcYjSYSE5OorKykpqa6zddXFO2GmivjGyldP6AoCunp6ZjNZjIyMqiubvsPkTeSm2iioxyO1lcXNDc3cfXqVUpLSykuLkZV1Q5tqGS3dzyjlK6fuHZM9ahRo3z+tFRZLiY6KjLyxpGuw2GnpqaaixcvUFRUSFFREdXVVYSFhZKUlMjgwYMID49o8/VVFZqbtc/pKCldP6IoCosXL2bo0KFkZmb65GmpNpuNkpISBgwYoHcU4YOSkmxUVzdQVlbGqVOnKCgooLy8HKPRRJ8+fRg8eDDx8Ql07RpLUJAJh6MJcAAOHA4bqtr6ELapSdu/ISam4xll7wU/oygKS5cuZdOmTSxYsIBdu3YR1tKtXC906tQp4uPjCb7Zmh8hvsLhcFBQUMD+/fs5cOAAR48epaHhDYKDo+jevTvh4WEoys3Hlpcvv87ly686//3q1d/Rrdtsunef3eLn1ddDSoprmWU/XT/lcDhYv349Fy5c4OWXXyY0NFTvSG3y/vvv889//pONGzfqHUV4IVVVOXfuHLm5ueTm5nLgwAFiYmIwm82YzWbuuOMOXnklipwcbUTqblVVsGcPfOMbrb+utf10pXT9mMPhYM2aNVRUVLBjxw6fGD3u2LGDuLg4nn76ab2jCC9RXl7OgQMHnEXb1NTkLFmz2UyPr+w0fvYsTJwIUVHa9ozu0tioPQ6cm3vzhy+u11rpyvSCH7t2WuqqVatYsmQJ27Zt8/rizc/PZ8aMGXrHEDqqq6vjyJEjzimD8+fPM3bsWMxmM9OnT6dfv36truGOj4fUVPj1r9032lVVbW3uunW3LtxbkZFuAGhubmbFihU0Nzfz4osvEhQUpHekm1JVlfvvv5933nmHuLg4veMID2lubuazzz5zjmTz8vIYPny4cyQ7fPjwG47daYuqKrj/fm3zmy5dXM9YUQFjx8Lbb7dt9CzTC4KmpiZ+8IMfOA/wM91q+3wdlJWV8eSTT/LBBx/oHUV0IofDQWFhobNkjxw5QmJiorNkR48e7ZZ7EIcPw7RpWkm6si3z1avaiPndd6FPn7a9R0pXAP8+tC8iIoL169e3e/TQ2f72t7/x1ltvsW/fPr2jCDcrLS3lwIEDzimDiIgIzGYzFouFcePGER3d9v0P2uOf/4SZM8Fm0/bEbc+T5Q6HVrjdumkj3OTktr9XSlc42Ww2Fi5cSFxcHGvXrvWq481/9KMfUV1dzfz58/WOIlxUWVnJwYMHnaPZuro6UlJSMJvNpKSk0KetQ0Y3KCqCBQvg88+1+diwsNbLV1WhpkZ76mzyZNiwQTu4sj2kdMUNGhoaWLBgAb1792bVqlVeU7zLly/n7rvv5pvf/KbeUUQ71dfX8+mnnzpL9syZM4wZMwaLxYLZbGbAgAG6bmBkt2sn+2ZlwYUL2lNlwcHar2tbNjY2aq8zGGDECJg/XzsRuCOxpXTF19TX1zN//nySkpJYvny5VxRvamoqW7dulcMofYDdbuf48ePOKYMTJ04wZMgQ57zsiBEjvPKGrcMBBw9qy74++QROndKeMgsNhaFDwWyGu+6CIUNc+xwpXXFTdXV1zJ07lyFDhrB06VJdRyL19fXcf//9fPzxx155ky/QqapKcXGxcyR7+PBhevfu7SzZMWPGyCGi15F1uuKmwsPD2bNnD+np6ezYsYNFixbpVryFhYX069dPCteLXLx40Vmyubm5BAcHY7FYeOihh1i5ciWx7Z3oFICUbsCLiIhg7969pKWlsXv3bubNm6dL8crOYvqrqqq64ebX1atXnTe/5syZQ9++fWVjeTeQ0hVERkaSlZWF1WolKyuL9PR0j//Hde20COE5jY2NHD161Fmyp06dYtSoUZjNZjZv3sygQYO8Yq7f30jpCgCioqLIyspizpw5BAUFMXt2yzstdYb8/Hzuv/9+j35moHE4HJw4ccJZssePH2fgwIGYzWYWLFjAbbfd5vWPifsDKV3hFBMTQ3Z2NnPmzMFkMvHMM8945HOvbc8n0wvupaoqp0+fdpbsoUOH6N69OxaLhe9+97vccccdRES0fQNv4R5SuuIGsbGxZGdnM3v2bEwmk0c2nyktLaVLly5ERUV1+mf5u0uXLt3w5JeiKJjNZu6//36WLVtGt27d9I4Y8KR0xdd069aNnJwcZ/FOmzatUz8vPz+fIa4ujAxQNTU1HDp0yDmavXLlCuPGjcNsNjNz5kwSEhLk5peXkdIVN9WjR48biveJJ57otM+Sm2htZ7PZOHbsmLNki4qKuO2227BYLKxbt46hQ4fKzS8vJ6UrWtSrV68bijc1NbVTPic/P5+HH364U67t6xwOB3l5ec4pg3/961/069fPefLzqFGj5OaXj5HSFa3q06cPOTk5zptrU6ZMcftnFBQUyCY3X1JVlbNnz95wHE1sbCxms5nHH3+cLVu2EOnKUbRCd1K64pbi4+NvWNXgzg1pqqurqaioID4+3m3X9DXl5eU3PPllt9tJSUnhnnvuYfHixV87jkb4Nild0SaJiYlkZWWRlpaG0WjkG7c6ma+NCgoKGDBgQEDNQ9bV1XHo0CHnlMHFixe54447sFgsPPXUUyQnJ8vNLz8mpSvarF+/fuzbt4/09HSMRiMPPPCAy9cMhPW5TU1NzuNo9u/fT0FBASNGjMBsNrNq1SqGDRvmdRvKi84jpSvaZcCAAezZs4e5c+diMpmYOHGiS9fLz89n2LBh7gnnJRwOBydPnnROF3z66ackJSWRkpLCnDlzGDVqlFuOoxG+SUpXtNvgwYPZtWsX8+fPx2g0cvfdd3f4WgUFBfznf/6nG9Ppo7S01PlAwoEDB+jSpQtms5nHHnuM9evXd9pxNML3SOmKDhk2bBgvv/wyCxYsYN26dUyYMKHd17Db7RQVFfnkpuUVFRXOHbn2799PQ0MDZrOZ8ePHk5mZSe/evfWOKLyUlK7osBEjRrB9+3YWL17Mxo0bMZvNLb+4sRHy8yEvDyorQVW5bLNxR1AQ4T5wE62+vp4jR444pwzOnTvH2LFjSUlJYerUqfTv319ufok2kZMjhMuOHDnC0qVL2bJlC3fccce/v6Gq8Omn8OMfw+9/D0ajdjhVczMAtU1N1NbX0yMuDh5+GL73PRgzpmOHUrlZc3Mzn3/+uXPK4MSJEwwbNoyUlBQsFgvDhw+XDddFi+S4HtHpDh48yLJly3jppZcYPXo0XLoEy5fDRx9pB1NFRWmle52LZWUYDAa6d+0KVVXaiYD33gubNoGH16aqqkpRUZGzZA8fPkzfvn2dx9GMHj1ajqMRbSalKzxi//79rFy5kuwZMxi4bRvU1UF0dIsj15IzZ+gaE/PvJ6xUFa5ehfBwyMmBDswTt8eFCxdueCghNDQUi8VCSkoKKSkpdO3atVM/X/gvOSNNeITFYmHHt75F6Jw5NPTuTegtSquhoYGQ65dOKQrExGhl/b3vweuvgwsrI77q2nE010azVVVVmM1mUlJSSEtLo2/fvm77LCFaIqUr3KekhJG7d1Pbsyclly6RGBbW4nrUZrsd1eG4+THd4eFQXw9z5mhzwUlJHYrT2NjIp59+6izZ06dPM3r0aMxmM9/61rcYOHBgQD0JJ7yDlK5wD4cDFi4Em42IuDh6BQdTUlJCYlISoSEhX3t545ej3BZvmYWFaascFi2Cd97R5ntvwW63f+04mkGDBmGxWFi4cCEjR468eckL4UFSusI9fvc7OHpUm8MFoiIjQVUpKSkhKTGRkK8Ub0Nj403L+AbR0do1f/tb+H//72vfVlWVU6dOOfcwOHz4MD169MBisTB9+nTGjh0rx9EIryOlK9wjJwdMphtumkVFRWnndJWUkJSURMh1+742NDQQcavVAIqiXTMnx1m6ZWVlNxxHYzAYsFgsPPjgg6xYsYK4uLhO+eMJ4S5SusJ1BQXaQw83OeMsOjoaFSg5fZpeCQm8XF5Obl0dZbW19IuIYL7JxIQuXVq8tD0sjMajR/nvJUv4Y3Ex5eXljBs3DovFwrPPPivH0QifI6UrXHf0qLbcq4Xyi4mO1qYCSkroFhVFTkICV4uKuNyzJ8vOneMX/frR58tRsENVqa+ro7aujtraWhobG4k1GBhcX89d69czZMgQufklfJqUrnDdwYNa6baia0wMqqry4JUrRHfpQmNICPdGRtInKIijVVUEKwq1tbXU19cTEhJCREQEPXr0ICwsDENlJT0iIsDPdiMTgUlKV7ju9Glow6qA2C/X7Z49cwaTycS/SkrIu3qVcLud5shIYrt2JTw+HuNXR7JBQdpnCOEHpHSF6+z2Nu+XENu1K4qiUFldza6GBlJ79ODeWz2UoCjO/RqE8HUyOSZcFxmpFW8bRUdH80ODgfCQEJb36XPrN9jt2mcI4QekdIXrxo6FpqY2vVRVVdafP8+V5ma2xcdjassIuakJrt+9TAgfJtMLwnW33QbXrcFtzeYLFyi22chKTCSkrasQgoO1zxDCD0jpCtelpGjbNjY1tXpD7XxTE7+prCRYUfhGQYHz6yt69eLhlo6zaWrSrt3aBulC+BApXeG68HCYOhXeeANiY1t8We+gIA62d9lXdTU8/bT2GUL4AZnTFe4xc6Y2DWCzue+aNpt2zZkz3XdNIXQmpSvco29fWLkSamtv+aBEm6iqdq3nn4f4eNevJ4SXkNIV7jNtGkya5Dx4ssNUVbvGfffB9OnuyyeEF5DSFe5jMMDevXDPPVBR0bEHGpqbtffefbd2LdlnQfgZ+YkW7hUSAq++CvPmadMDlZXaBue34nBor62r0977wx9CC6dOCOHLpHSF+wUFwYIF8D//A3feqa1AKC/XTvy12bSCdTi05WBVVf/+3p13wrvvau+VEx6En5IlY6LzDBsGP/sZnDmjnf7wySdw7JhWsKDtv3vnnTB+PDzyCCQm6ptXCA+QI9iFEMLNWjuCXaYXhBDCg6R0hRDCg6R0hRDCg6R0hRDCg6R0hRDCg6R0hRDCg6R0hRDCg1pdp6soyiVAjmEVQoj2SVJVtfvNvtFq6QohhHAvmV4QQggPktIVQggPktIVQggPktIVQggPktIVQggP+v/CiVtxc1YEEgAAAABJRU5ErkJggg==\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -638,22 +676,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
+   "execution_count": 14,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T18:01:21.087401Z",
+     "start_time": "2019-12-12T18:01:20.890621Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
       "distance\n",
-      " [[ 0. 26. 25.]\n",
-      " [26.  0. 28.]\n",
-      " [25. 28.  0.]]\n"
+      " [[ 0. 37. 77.]\n",
+      " [37.  0. 40.]\n",
+      " [77. 40.  0.]]\n"
      ]
     },
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -685,20 +728,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
+   "execution_count": 15,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T18:01:21.923866Z",
+     "start_time": "2019-12-12T18:01:21.722088Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "order = (0, 1, 2) Distance = 79.0\n",
-      "Best order from brute force = (0, 1, 2) with total distance = 79.0\n"
+      "order = (0, 1, 2) Distance = 154.0\n",
+      "Best order from brute force = (0, 1, 2) with total distance = 154.0\n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -751,8 +799,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
+   "execution_count": 16,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T18:01:23.125345Z",
+     "start_time": "2019-12-12T18:01:23.103970Z"
+    }
+   },
    "outputs": [],
    "source": [
     "qubitOp, offset = tsp.get_tsp_qubitops(ins)\n",
@@ -769,8 +822,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
+   "execution_count": 17,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T18:01:23.539137Z",
+     "start_time": "2019-12-12T18:01:23.526739Z"
+    }
+   },
    "outputs": [],
    "source": [
     "# Create an instance of a model and variables\n",
@@ -790,8 +848,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
+   "execution_count": 18,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T18:01:24.017655Z",
+     "start_time": "2019-12-12T18:01:23.985994Z"
+    }
+   },
    "outputs": [],
    "source": [
     "qubitOp_docplex, offset_docplex = docplex.get_qubitops(mdl)"
@@ -806,23 +869,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
+   "execution_count": 19,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T18:01:24.809497Z",
+     "start_time": "2019-12-12T18:01:24.598234Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "energy: -600039.5\n",
-      "tsp objective: 79.0\n",
+      "energy: -600077.0\n",
+      "tsp objective: 154.0\n",
       "feasible: True\n",
-      "solution: [1, 2, 0]\n",
-      "solution objective: 79.0\n"
+      "solution: [0, 1, 2]\n",
+      "solution objective: 154.0\n"
      ]
     },
     {
      "data": {
-      "image/png": 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uIKErNGc0Grn99tu5/fbbKSgowGazsXnzZux2u6sBT//+/bUuU3RgHg1dmSAhvEmfPn1YuXIld999N0ePHsVqtbJy5Ur69u3rasATGRmpdZmig9FkpSuEN9HpdNxyyy3ccsstrgY8qamp/Md//Afjxo1zNeCRexGiLUjoCnEFPz8/EhMTSUxMpLKykvT0dN555x22bt3K1KlTsVgsjB49WhrwiOsmoSvEVYSGhjJz5kxmzpxJUVERaWlpPPfcc1RUVGA2mzGbzdKAR7Sax0M3Pz/fkx8pRJvo0aMHy5YtY9myZZw8eRKr1coDDzxAt27dUFWVGTNm0K1bN63LFD7Aoz8jyUpXdASDBw9m3bp1pKamsnbtWk6cOMEdd9zBT3/6U2w2G9XV1VqXKLyYx1e6Mj1CdBR6vZ74+Hji4+OpqakhIyOD1NRUnn32WRRFQVVV4uPjZf9XfI+sdIVoA0FBQcyYMYNXXnmFd955h6FDh/Laa6+hqiq//e1vOXHihDTgEYDcSBOizXXt2pWFCxeycOFCvv76a6xWKw8//DDBwcGuBzB69uypdZlCIx7rvQBQXV3NrbfeSnZ2dptdUwhfYLfbOXToEFarlX/+858MHjwYi8XCtGnTCAsL07o80ca8ouENOJveJCYmkp6eTlBQUJtdVwhfUldXx+7du7Farezbt49JkyZhNpuZNGkSfn4e/eFTtJOWQteje7pXNr0RorMKCAhg2rRp/OY3v+H9998nLi6OP//5z5jNZp577jmOHDki+78dmMf/t9oUur169fL0RwvhdYxGI3PnzmXu3LmcO3cOm83GE088AeB6AKNfv34aVynakmahK4T4vr59+3LPPfewcuVKvvjiC1JTU1mxYgX9+/d3NeCJiIjQukxxgyR0hfAyOp2OYcOGMWzYMNavX09OTg42m41XX32V8ePHo6oqkydPlgY8PkqT0JUHJIRwj5+fHyaTCZPJREVFBenp6bz99ts89dRTTJ8+HbPZLA14fIysdIXwEWFhYcyaNYtZs2ZRWFhIWloa27Zto7q6GrPZjKqqREdHa12muAZNQvfUqVOe/lghOpSePXuyfPny7zXgWbNmDVFRUa4GPF27dtW6TNEMWekK4cN0Oh2xsbHExsaydu1a9u3bR2pqKq+//jojR45EVVWmTJki5+K9iISuEB2EXq9nwoQJTJgwgerqanbt2oXVanU14LFYLMTFxcn+r8YkdIXogIKDg13nfIuLi9mxYwevvPIKxcXFpKSkYDabiY2N1brMTklCV4gOrlu3bixatIhFixaRl5eHzWZj/fr1hIWFoaoqKSkpMqHbgzzaewGgtraWKVOmsGfPHnQ6XZteWwjhHrvdzqefforVaiU9PZ2bb74ZVVWZNm0aoaGhWpfn87ym4U0Tk8nEjh07CAkJafNrCyFap66ujqysLFJTUzlw4ACJiYmoqsrEiROlAc91ail0Nfkn2rTFIKErhPYCAgKYPn0606dPp6SkhJ07d/LGG2+wZcsWkpOTsVgsDB06VH4ybSOahm6fPn20+HghxFVERkYyb9485s2bx9mzZ7FarWzatAm9Xo/FYsFsNsuf2xukaegKIbxXv379WL16NatWreLIkSPYbDaWLVtGdHQ0qqpy6623YjQatS7T50joCiFapNPpGDFiBCNGjOChhx4iJycHq9XKyy+/THx8PKqqkpiYKA143KRJ6EZEREjoCuGD/P39URQFRVEoLy8nPT2dN99809WAR1VVRo0aJfu/LZCVrhDiuoSHhzN79mxmz57NhQsXSEtLY+vWrdTU1Lga8AwcOFDrMr2OJs8DSugK0bH06tWLu+66i7feeovnn3+e6upqVq9ezbJly3jzzTe5dOmS1iV6DVnpCiHajE6n4+abb+bmm29m3bp17N27F5vNxh/+8AdGjRqFxWJBUZRO3YBHQlcI0S4MBgMJCQkkJCRQVVXFrl272L59O8888wxTpkzBbDZ3ygY8ErpCiHYXEhKCqqqoqso333zDjh07ePnll7l8+TIpKSmoqsqgQYO0LtMjNAtdGdkjROfUvXt3Fi9ezOLFi8nLy8NqtbJu3TqMRqOrAbsmDXguXoSjR+H8eWhshKAgGDQIYmOdf99GNOm9UF9fj8lkIicnR46WCCGw2+0cPHiQ1NRUdu3axZAhQ1wNeNq1XUBJCbz7Lvznf0JhIfj5QUMDOByg14PBAPX1MHEirFoFJpPz967B6xreACQlJZGamkpYWFi7XF8I4Ztqa2vJzMzEZrPxySefkJiYiMViYcKECRjcCDy32O3w97/DU085QzUw0LmabW4RaLdDebnz76Oj4eWX4ZZbWry8V4burFmzeO211+jXr1+7XF8I4fsuX77Mzp07sVqtFBQUMGPGDMxm84014CkthXvvhX37IDQU3H2SzuFwhq/DAb/4hXPle5UavK7LGHx3M01CVwhxNV26dGH+/PnMnz+f/Px8bDYbGzduxN/f39WAvVUNeEpLYd48+OoriIy8amg2S6cDo9G5/fDss1BRAevXt/p70uyshpxgEEK0xoABA7j33nt57733eOKJJygqKmLp0qWsWrWKd999l7KyspYvYLfDmjXXF7hX8vNzhu/vfufcD24lCV0hhE/R6XSMHDmSRx55hLS0NJYsWUJOTg4zZ85kw4YN7Nq1i/r6+h+/8a23YO/eGwvcJgYDBAfDY4/BhQutequErhDCZ/n7+5OUlMRzzz3HBx98QEJCAn/7299ISUnhmWee4fDhwzgcDue2wpNPQkjIjQduk6AgqKmBp59u1ds039MVQoi2EB4ezpw5c5gzZw4FBQWkpaWxZcsW6uvrWdelC6aqKvy7d2/bDzUaYccO5xnfqCi33qJp6J4/f16rjxdCdGB9+vTh7rvvZsWKFRw7doxwi4VzFy/iqKggIiICo9GI3w+On9XZ7Wy7cIG9VVWUNTbSz9+fB3v0YFJLx1oNBueDFO+95zzN4AbZXhBCdFg6nY6hPXvST6dj4JAhRHXvTnVVFV99+SVnzpyhrKwM+7fHZhuBXv7+/HHAAHbFxnJfVBSPnDtHQV1dyx/i5we7drldk2wvCCE6tmPHwN8fnU5HWFgYYWFhNNrtlJeXc7mkhPMXLhAeHk5ERASroqJo2vE1hYfTx9+fYzU19GnpLG9QEHz2mfP8rhv7xRK6QoiOramXwhUMej2RERFERkRQ39BAWVkZhYWFNDY0YDQaiYyMpNJgIL+ujpjAwJav7+cHly5Bba1bPRokdIUQHVtjo3MVehX+fn5ERERgMBgoKSmh6OJFHHo9W6qr+beICKKvFbo6nbNPQ0ODW+VoFrpGo5HS0lLsdnun66cphPCgoCBnKF7BAdTV1lJeUUFFeTk1tbWEhYYSGRlJn7592VxYiB+woVeva1/f4XD+crMTmWah6+fnR2hoKBUVFTLGWQjRfgYNAr0eh8NBVVWVK2gdDgdh4eF0j4oiJCQEvU6Hw+Fgy/nzFDc08Er//vi5c6a3thb69XNuM7hBs9CF77YYJHSFEO2hrKyMj/PyGFdYSHFDA/4BAYSFh9OvXz8Cg4L4YaQ+c+ECp+rqeG3AAALd/Qm8pgbGjnW7Jq8I3QEDBmhZhhCiAzlz5gxZWVlkZmbyxRdfMG7cOGLHjiUmLw+/bt2u+r7z9fW8U1JCgE7HjJMnXb+/qVcvzBERV/9AnQ4sFrfr84rQFUKI62W32/nss8/IyMggKyuL0tJSFEVh0aJFxMfHO4dg7tkDy5e3eKyrt78/+4cObd2H19ZCeDhMner2WyR0hRA+p6qqio8//pjMzEx2795NVFQUJpOJzZs3M3To0B/fnJ84EQYPhpMnoaVVa2s4HFBVBU884fZ+LkjoCiF8RGFhIZmZmWRmZnLo0CFGjBhBUlIS9957L7179275zXo9vPQS/Nu/OSdF+PvfeEGlpTByJCxd2qq3aRq6ERERErpCiGbZ7XaOHTvmCtrCwkISExP593//d7Zt20ZoaGjrLhgb62zFuHmzs1FNK1anP1Je7pw68fLLPzqOdi2ar3Tz8/O1LEEI4UVqa2vZu3cvmZmZZGVlERoaSlJSEr/4xS8YOXLkjc9IW7bMOfHhhRec/XBbO+W3qU1kaKhzxtp1HALQPHRlpStE51ZcXOw6bbB//36GDBmCoigsXbq0fU423X8/DBwIGzc6pwGHh7s14ZeaGqiuhlGjnFsV11mbhK4QwqMcDgdffvklWVlZZGRkkJ+fz8SJE0lOTmbz5s2eObdvsUB8PGzbBu+/73xU2GBwrnz9/Z0nHOx25+mEmhrna0YjbNgAS5a0ekvhShK6Qoh2V19fzyeffOI61qXT6VAUhQceeIAxY8bg3xY3tlorKsq5zfDoo7B9O2RkwOHDUFzsDF2dzjlyfdw4SEmBpKQb2wf+loSuEKJdlJaWkp2dTUZGBrm5udx0000oisJLL71ETEzM9Y9Qb2tdu8Jddzl/gfN0Q2OjczR7O/SF0TR0jUYjlZWV0vRGiA7i9OnTrtXs8ePHiY+PR1EUfvnLX9K1a1ety3OPv3/bHCm7Ck1DV6/XExYWRllZGZGRkVqWIoS4Do2NjRw6dMh1rKu6uhqTycRdd91FXFwcgddqi9gJaRq68N0Wg4SuEL6hoqKCnJwcMjMzyc7Opnfv3iQlJfH0008zZMgQ79k28FJeE7pCCO9VUFDgWs0eOXKE0aNHk5SUxE9/+lN69OihdXk+RfPQlafShPA+drudzz//3BW0ly5dwmQyMW/ePH7zm98QEhKidYk+S/PQlZWuEN6hurqa3NxcVxOZyMhIFEXh0UcfZfjw4XKzu41I6ArRiRUVFbF7924yMjI4ePAgw4YNQ1EUVq5cSd++fbUur0PyitC9dOmS1mUI0Sk4HA5OnDjhOtZ17tw5Jk2ahMVi4amnniI8PFzrEjs8rwjdvLw8rcsQosOqq6tj//79rqANDAxEURR+9rOfMWrUKPza4Ckr4T7N/2lHRkZSWlqqdRlCdCiXLl1i9+7dZGVlsXfvXgYPHoyiKLz22msMHDhQjnVpyCtCV/Z0hbgxDoeDU6dOuU4b5OXlER8fz5QpU3j00UflHLwXkdAVwkc1NDRw8OBBV9A2NDSQlJTEqlWrGDduHAEBAVqXKJohoSuEDykrK2PPnj1kZmaSk5ND//79SUpK4vnnn2fw4MGybeADNA/dsLAwqqqqaGhokA19IZpx5swZ12r26NGjxMXFYTKZWL9+Pd27d9e6PNFKmqecXq/HaDRSVlbmO12IhGhHdrudw4cPu4K2vLwck8nE4sWLvxspLnyW5qEL320xSOiKzqpppHhGRgbZ2dlERUWhKAq//vWvmx8pLnyWV4WuEJ3JhQsXXAMYDx06xMiRI1EUhTVr1lx7pLjwWRK6QnhI00jxpocUCgsLmTx58vWPFBc+SUJXiHZUU1PD3r17XdNuw8PDURSFDRs2MHLkSNk26IQkdIVoY82NFE9KSmq/keLCp3hN6BYWFmpdhhDXpWmkeNNpg/z8fBISEjw7Ulz4DK8J3ePHj2tdhhBuq6ur45NPPnEFrV6vJykpSduR4sIneEXoyvQI4QtKSkrIzs4mMzOT3NxcYmJiMJlMvPLKK9x0003yNJhwi1eEruzpCm/kcDg4ffq0azV78uRJxo8f73sjxYVXkdAV4gpNI8WbjnXV1NRgMplYsWIF48ePlyYy4oZJ6IpOr6Kigj179pCVlUV2djZ9+vRBURS2bt3KzTffLNsGok15ReiGhoZSV1dHXV2drCSER5w7d871NNiRI0cYM2YMiqLISHHR7rwidHU6HREREZSWlhIVFaV1OaIDstvtHDlyhKysLDIyMigpKWHy5MnMnz9fRooLj/KK0IXvxvZI6Iq20txI8aSkJB577DEZKS4041WhK/u64kYVFRW5ngY7ePAgw4cPx2QyyUhx4TUkdIVPczgcHD9+3HWsq6CggMTERCwWC08//TRhYWFalyjE93hN6MoDEsJddXV17Nu3z3UjrGmk+Pr16xk1ahQGg0HrEoW4Kq8JXVnpipY0jRTPzMxk3759MlJc+CyvCt2CggKtyxBeommkeNNDCnl5eUyYMIGpU6fy2GOPyUhx4bO8Kg7PmnAAAAibSURBVHS/+OILrcsQGmpoaPheExm73Y6iKKxevZqxY8fKGW7RIXhV6Mr2QudTVlZGdnY2WVlZ5OTkMGDAABRF4YUXXmDQoEGybSA6HAld4XH5+fmuhxSOHTtGXFyc60aYjBQXHZ1XhW5paanWZYh20NxIcUVRWLJkiYwUF52OV4WurHQ7jqqqKnJycsjMzCQ7O5sePXqgKApbtmxhyJAh8jSY6LS8JnSDg4NpbGyktraWwMBArcsR1+H8+fOup8EOHz7MqFGjMJlM3HffffTq1Uvr8oTwCl4Tulc2vZEuT77Bbrdz9OhR17bBxYsXmTx5MrfffjvPPfecNJERohleE7rw3RaDhK73ahop3vQ0mNFoxGQy8cgjjzBixAjZNhDiGrwydIV3+eabb8jKyiIrK4v9+/czdOhQkpKSWL58Of3799e6PCF8ioSu+BGHw8HJkydd2wZnzpwhISGB2267TUaKC3GDJHQF4Gwic+DAAdeNMIPB4JqkMGbMGPz8vOo/FSF8llf9SZLQ9aymkeIZGRns3buXmJgYFEWRkeJCtCOvC93Tp09rXUaH1dxI8fj4eBRF4ZFHHpGR4kJ4gNeF7qFDh7Quo0NpbGzk008/dQVtbW0tiqJw9913ExcXJ01khPAwrwtd2V64ceXl5a6nwfbs2UPfvn0xmUxs27aN2NhY2TYQQkMSuh1E00jxzMxMPv/8c8aOHYuiKKxdu1bOPQvhRbwqdGVkj/uaRoo3BW3TSPEFCxYQHx9PcHCw1iUKIZrhVaHbtNJ1OBzyI3AzqqqqyM3NdT2o0LVrV5KSknj88ccZNmyYPA0mhA/wqtANCgpCp9NRU1MjK7VvFRUVuVazn376KcOHD0dRFO655x769OmjdXlCiFbyqtCF71a7nTV0HQ4Hx44dczX5Pn/+PJMnT2bmzJls3bpVRooL4eO8NnR79+6tdSke0zRSPCMjg927dxMUFISiKDz88MMyUlyIDsYrQ7czTJD44Ujx2NhYFEXh97//PQMHDtS6PCFEO/HK0O2IJxgcDgd5eXmukeKnTp1i4sSJTJs2jccff5yIiAitSxRCeICEbjuqr6/n4MGDrhthDocDk8nEmjVrGDt2LP7+/lqXKITwMAndNtY0UjwzM5OPP/6YgQMHoigKL774Ij/5yU/kKJwQnZzXhW5ERAR5eXlal9Eq+fn5rtVs00jxpKQkfv7zn9OtWzetyxNCeBHvCl2Hg5719Vw4fhwOHICwMLjpJvCypiyNjY0cPnzYdayrsrISRVFYunQp8fHxMlhTCHFV2oduQwPs2gV/+Qvs3098RQWx5eXw8cfgcDhfj4mB+fPh9ttBo/aDlZWV3xsp3rNnTxRF4cknn5SR4kIIt+kcDsdVX4yLi3Ps37+//T59507YtAma9nBDQ6lpaOBcQQE/iYlx/p7dDjU1UFcHej0sXw7r14MHHp5oGimekZHBZ599xujRozGZTJhMJhkpLoS4Kp1Od8DhcMQ195o2K93KSvjlL8Fmg8BAuOK4lAHnj+8uej2EhDh/NTTAn/4EaWnw+utwyy1tWlbTSPGmY11NI8Xnzp3L888/LyPFhRA3zPOhW14OS5bAZ59BZKQzVK9gMBhobGzEAfzoPr+fn/M9hYUwbx789a8wduwNldPcSPGmSQoyUlwI0dY8G7p2O9x7Lxw5Al26QDPHp/Q6HTqdDrvdjuFqgWc0OlfLy5fD++9DdHSryrh48aKrU9eBAwe45ZZbUBRFRooLIdqdZ0P3v/8bcnOdq9UWzqs2rXavGroAoaFQWgoPPQT/+MePVsxX+uFI8bNnzzJx4kRSUlL49a9/LSPFhRAe47nQLSyErVudYXmNBwT8vg1drvXEltEIhw/D22/DggXfe6lppHhT0Pr5+ZGUlMTatWsZPXq0jBQXQmjCc8nz5pvOEwihodf8UoPBQGNDw7WvqdM5b8S9+irMn8/l0lLX02BNI8WTkpJ49dVXZaS4EMIreCZ0m04duHH3v6yxka1VVRwqL6dbYCAPRkWRcpVmMA6gTq+n9quveHX2bD4sK2PChAmYTCYZKS6E8EqeCd2vvnKetXWjAfe2Cxfw1+t5u3t3ikNCWHfmDLFBQcR8+5SXw+Ggqrqa8vJyKsrLcTgcRBkMLIqO5hcvvCAjxYUQXs0zoXv0qPPkwjVU2+2kl5fz+8hIAh0ORoeEkBQezvslJdwVHExFeTkVlZUE+PsTFh5Ov379CAwKQldRQWRRkdc9LiyEED/kmdDNy3Pu515Dfl0dBiA6MJDKykqKL12iS0UFn9TUMKdLF8LDw+nRsyf+P7wJFhDgXE0LIYSX80zoVlW1eKTL9WV2O6F6PcEhIRQXF6M3GOgZFkZAQAADWjo/q9e7FepCCKE1z4RuSIhb2wshej2VdjtBgYEMGjQIAHtxMWFXPhbcHLtdthaEED7BM8+4/uQnboXigIAAGnFuMzQ5UVvruol2VXV1zs8QQggv55nQHTr0mg9EAATr9UwLD+cPFy9SbbdzqKqKjPJyLNeaH1ZbCxMntlGxQgjRfjy30g0NdYbjNTzSqxe1djvJJ06w6dw5Nvbq1fJK1+FwNsJJTGzDgoUQon14Zk/XYIC774bf/tb5BFkLjAYDL7Sm6Ux1NfTuDXHNtq4UQgiv4rm+hfPnO/d12/KUgcPhvN6DD7p1OkIIIbTmuaTq0QMef9zZkrGFaRWtUloKo0fDHXe0zfWEEKKdeXZ5uGABJCQ4x/PcaPBWVDhH9rz4oqxyhRA+w7Nppdc7x+yMGuUMXjfO7jarrMx58+y//gsGDGjbGoUQoh15fokYGuoMy5kzndsDVVXuv7ehwRnWffrA//6vM7yFEMKHtDgNWKfTXQROe64cIYToEAY6HI6o5l5oMXSFEEK0LbkDJYQQHiShK4QQHiShK4QQHiShK4QQHiShK4QQHvT/AXZyCZ93hy7iAAAAAElFTkSuQmCC\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -867,8 +935,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 21,
    "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T18:03:05.816636Z",
+     "start_time": "2019-12-12T18:02:14.760335Z"
+    },
     "scrolled": true
    },
    "outputs": [
@@ -876,16 +948,16 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "energy: -581599.7061630379\n",
-      "time: 148.96690106391907\n",
+      "energy: -592830.143529644\n",
+      "time: 50.77348875999451\n",
       "feasible: True\n",
-      "solution: [0, 1, 2]\n",
-      "solution objective: 79.0\n"
+      "solution: [1, 0, 2]\n",
+      "solution objective: 154.0\n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -926,7 +998,7 @@
     "    'algorithm': algorithm_cfg,\n",
     "    'optimizer': optimizer_cfg,\n",
     "    'variational_form': var_form_cfg,\n",
-    "    'backend': {'provider': 'qiskit.BasicAer', 'name': 'statevector_simulator'}\n",
+    "    'backend': {'provider': 'qiskit.Aer', 'name': 'statevector_simulator'}\n",
     "}\n",
     "result = run_algorithm(parahms,algo_input)\n",
     "\"\"\"\n",
@@ -943,23 +1015,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
+   "execution_count": 22,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T18:21:15.090052Z",
+     "start_time": "2019-12-12T18:03:05.895711Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "energy: -588321.16796875\n",
-      "time: 1546.7725381851196\n",
+      "energy: -596561.46484375\n",
+      "time: 1088.9133131504059\n",
       "feasible: True\n",
-      "solution: [2, 1, 0]\n",
-      "solution objective: 79.0\n"
+      "solution: [2, 0, 1]\n",
+      "solution objective: 154.0\n"
      ]
     },
     {
      "data": {
-      "image/png": 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uIKErNGc0Grn99tu5/fbbKSgowGazsXnzZux2u6sBT//+/bUuU3RgHg1dmSAhvEmfPn1YuXIld999N0ePHsVqtbJy5Ur69u3rasATGRmpdZmig9FkpSuEN9HpdNxyyy3ccsstrgY8qamp/Md//Afjxo1zNeCRexGiLUjoCnEFPz8/EhMTSUxMpLKykvT0dN555x22bt3K1KlTsVgsjB49WhrwiOsmoSvEVYSGhjJz5kxmzpxJUVERaWlpPPfcc1RUVGA2mzGbzdKAR7Sax0M3Pz/fkx8pRJvo0aMHy5YtY9myZZw8eRKr1coDDzxAt27dUFWVGTNm0K1bN63LFD7Aoz8jyUpXdASDBw9m3bp1pKamsnbtWk6cOMEdd9zBT3/6U2w2G9XV1VqXKLyYx1e6Mj1CdBR6vZ74+Hji4+OpqakhIyOD1NRUnn32WRRFQVVV4uPjZf9XfI+sdIVoA0FBQcyYMYNXXnmFd955h6FDh/Laa6+hqiq//e1vOXHihDTgEYDcSBOizXXt2pWFCxeycOFCvv76a6xWKw8//DDBwcGuBzB69uypdZlCIx7rvQBQXV3NrbfeSnZ2dptdUwhfYLfbOXToEFarlX/+858MHjwYi8XCtGnTCAsL07o80ca8ouENOJveJCYmkp6eTlBQUJtdVwhfUldXx+7du7Farezbt49JkyZhNpuZNGkSfn4e/eFTtJOWQteje7pXNr0RorMKCAhg2rRp/OY3v+H9998nLi6OP//5z5jNZp577jmOHDki+78dmMf/t9oUur169fL0RwvhdYxGI3PnzmXu3LmcO3cOm83GE088AeB6AKNfv34aVynakmahK4T4vr59+3LPPfewcuVKvvjiC1JTU1mxYgX9+/d3NeCJiIjQukxxgyR0hfAyOp2OYcOGMWzYMNavX09OTg42m41XX32V8ePHo6oqkydPlgY8PkqT0JUHJIRwj5+fHyaTCZPJREVFBenp6bz99ts89dRTTJ8+HbPZLA14fIysdIXwEWFhYcyaNYtZs2ZRWFhIWloa27Zto7q6GrPZjKqqREdHa12muAZNQvfUqVOe/lghOpSePXuyfPny7zXgWbNmDVFRUa4GPF27dtW6TNEMWekK4cN0Oh2xsbHExsaydu1a9u3bR2pqKq+//jojR45EVVWmTJki5+K9iISuEB2EXq9nwoQJTJgwgerqanbt2oXVanU14LFYLMTFxcn+r8YkdIXogIKDg13nfIuLi9mxYwevvPIKxcXFpKSkYDabiY2N1brMTklCV4gOrlu3bixatIhFixaRl5eHzWZj/fr1hIWFoaoqKSkpMqHbgzzaewGgtraWKVOmsGfPHnQ6XZteWwjhHrvdzqefforVaiU9PZ2bb74ZVVWZNm0aoaGhWpfn87ym4U0Tk8nEjh07CAkJafNrCyFap66ujqysLFJTUzlw4ACJiYmoqsrEiROlAc91ail0Nfkn2rTFIKErhPYCAgKYPn0606dPp6SkhJ07d/LGG2+wZcsWkpOTsVgsDB06VH4ybSOahm6fPn20+HghxFVERkYyb9485s2bx9mzZ7FarWzatAm9Xo/FYsFsNsuf2xukaegKIbxXv379WL16NatWreLIkSPYbDaWLVtGdHQ0qqpy6623YjQatS7T50joCiFapNPpGDFiBCNGjOChhx4iJycHq9XKyy+/THx8PKqqkpiYKA143KRJ6EZEREjoCuGD/P39URQFRVEoLy8nPT2dN99809WAR1VVRo0aJfu/LZCVrhDiuoSHhzN79mxmz57NhQsXSEtLY+vWrdTU1Lga8AwcOFDrMr2OJs8DSugK0bH06tWLu+66i7feeovnn3+e6upqVq9ezbJly3jzzTe5dOmS1iV6DVnpCiHajE6n4+abb+bmm29m3bp17N27F5vNxh/+8AdGjRqFxWJBUZRO3YBHQlcI0S4MBgMJCQkkJCRQVVXFrl272L59O8888wxTpkzBbDZ3ygY8ErpCiHYXEhKCqqqoqso333zDjh07ePnll7l8+TIpKSmoqsqgQYO0LtMjNAtdGdkjROfUvXt3Fi9ezOLFi8nLy8NqtbJu3TqMRqOrAbsmDXguXoSjR+H8eWhshKAgGDQIYmOdf99GNOm9UF9fj8lkIicnR46WCCGw2+0cPHiQ1NRUdu3axZAhQ1wNeNq1XUBJCbz7Lvznf0JhIfj5QUMDOByg14PBAPX1MHEirFoFJpPz967B6xreACQlJZGamkpYWFi7XF8I4Ztqa2vJzMzEZrPxySefkJiYiMViYcKECRjcCDy32O3w97/DU085QzUw0LmabW4RaLdDebnz76Oj4eWX4ZZbWry8V4burFmzeO211+jXr1+7XF8I4fsuX77Mzp07sVqtFBQUMGPGDMxm84014CkthXvvhX37IDQU3H2SzuFwhq/DAb/4hXPle5UavK7LGHx3M01CVwhxNV26dGH+/PnMnz+f/Px8bDYbGzduxN/f39WAvVUNeEpLYd48+OoriIy8amg2S6cDo9G5/fDss1BRAevXt/p70uyshpxgEEK0xoABA7j33nt57733eOKJJygqKmLp0qWsWrWKd999l7KyspYvYLfDmjXXF7hX8vNzhu/vfufcD24lCV0hhE/R6XSMHDmSRx55hLS0NJYsWUJOTg4zZ85kw4YN7Nq1i/r6+h+/8a23YO/eGwvcJgYDBAfDY4/BhQutequErhDCZ/n7+5OUlMRzzz3HBx98QEJCAn/7299ISUnhmWee4fDhwzgcDue2wpNPQkjIjQduk6AgqKmBp59u1ds039MVQoi2EB4ezpw5c5gzZw4FBQWkpaWxZcsW6uvrWdelC6aqKvy7d2/bDzUaYccO5xnfqCi33qJp6J4/f16rjxdCdGB9+vTh7rvvZsWKFRw7doxwi4VzFy/iqKggIiICo9GI3w+On9XZ7Wy7cIG9VVWUNTbSz9+fB3v0YFJLx1oNBueDFO+95zzN4AbZXhBCdFg6nY6hPXvST6dj4JAhRHXvTnVVFV99+SVnzpyhrKwM+7fHZhuBXv7+/HHAAHbFxnJfVBSPnDtHQV1dyx/i5we7drldk2wvCCE6tmPHwN8fnU5HWFgYYWFhNNrtlJeXc7mkhPMXLhAeHk5ERASroqJo2vE1hYfTx9+fYzU19GnpLG9QEHz2mfP8rhv7xRK6QoiOramXwhUMej2RERFERkRQ39BAWVkZhYWFNDY0YDQaiYyMpNJgIL+ujpjAwJav7+cHly5Bba1bPRokdIUQHVtjo3MVehX+fn5ERERgMBgoKSmh6OJFHHo9W6qr+beICKKvFbo6nbNPQ0ODW+VoFrpGo5HS0lLsdnun66cphPCgoCBnKF7BAdTV1lJeUUFFeTk1tbWEhYYSGRlJn7592VxYiB+woVeva1/f4XD+crMTmWah6+fnR2hoKBUVFTLGWQjRfgYNAr0eh8NBVVWVK2gdDgdh4eF0j4oiJCQEvU6Hw+Fgy/nzFDc08Er//vi5c6a3thb69XNuM7hBs9CF77YYJHSFEO2hrKyMj/PyGFdYSHFDA/4BAYSFh9OvXz8Cg4L4YaQ+c+ECp+rqeG3AAALd/Qm8pgbGjnW7Jq8I3QEDBmhZhhCiAzlz5gxZWVlkZmbyxRdfMG7cOGLHjiUmLw+/bt2u+r7z9fW8U1JCgE7HjJMnXb+/qVcvzBERV/9AnQ4sFrfr84rQFUKI62W32/nss8/IyMggKyuL0tJSFEVh0aJFxMfHO4dg7tkDy5e3eKyrt78/+4cObd2H19ZCeDhMner2WyR0hRA+p6qqio8//pjMzEx2795NVFQUJpOJzZs3M3To0B/fnJ84EQYPhpMnoaVVa2s4HFBVBU884fZ+LkjoCiF8RGFhIZmZmWRmZnLo0CFGjBhBUlIS9957L7179275zXo9vPQS/Nu/OSdF+PvfeEGlpTByJCxd2qq3aRq6ERERErpCiGbZ7XaOHTvmCtrCwkISExP593//d7Zt20ZoaGjrLhgb62zFuHmzs1FNK1anP1Je7pw68fLLPzqOdi2ar3Tz8/O1LEEI4UVqa2vZu3cvmZmZZGVlERoaSlJSEr/4xS8YOXLkjc9IW7bMOfHhhRec/XBbO+W3qU1kaKhzxtp1HALQPHRlpStE51ZcXOw6bbB//36GDBmCoigsXbq0fU423X8/DBwIGzc6pwGHh7s14ZeaGqiuhlGjnFsV11mbhK4QwqMcDgdffvklWVlZZGRkkJ+fz8SJE0lOTmbz5s2eObdvsUB8PGzbBu+/73xU2GBwrnz9/Z0nHOx25+mEmhrna0YjbNgAS5a0ekvhShK6Qoh2V19fzyeffOI61qXT6VAUhQceeIAxY8bg3xY3tlorKsq5zfDoo7B9O2RkwOHDUFzsDF2dzjlyfdw4SEmBpKQb2wf+loSuEKJdlJaWkp2dTUZGBrm5udx0000oisJLL71ETEzM9Y9Qb2tdu8Jddzl/gfN0Q2OjczR7O/SF0TR0jUYjlZWV0vRGiA7i9OnTrtXs8ePHiY+PR1EUfvnLX9K1a1ety3OPv3/bHCm7Ck1DV6/XExYWRllZGZGRkVqWIoS4Do2NjRw6dMh1rKu6uhqTycRdd91FXFwcgddqi9gJaRq68N0Wg4SuEL6hoqKCnJwcMjMzyc7Opnfv3iQlJfH0008zZMgQ79k28FJeE7pCCO9VUFDgWs0eOXKE0aNHk5SUxE9/+lN69OihdXk+RfPQlafShPA+drudzz//3BW0ly5dwmQyMW/ePH7zm98QEhKidYk+S/PQlZWuEN6hurqa3NxcVxOZyMhIFEXh0UcfZfjw4XKzu41I6ArRiRUVFbF7924yMjI4ePAgw4YNQ1EUVq5cSd++fbUur0PyitC9dOmS1mUI0Sk4HA5OnDjhOtZ17tw5Jk2ahMVi4amnniI8PFzrEjs8rwjdvLw8rcsQosOqq6tj//79rqANDAxEURR+9rOfMWrUKPza4Ckr4T7N/2lHRkZSWlqqdRlCdCiXLl1i9+7dZGVlsXfvXgYPHoyiKLz22msMHDhQjnVpyCtCV/Z0hbgxDoeDU6dOuU4b5OXlER8fz5QpU3j00UflHLwXkdAVwkc1NDRw8OBBV9A2NDSQlJTEqlWrGDduHAEBAVqXKJohoSuEDykrK2PPnj1kZmaSk5ND//79SUpK4vnnn2fw4MGybeADNA/dsLAwqqqqaGhokA19IZpx5swZ12r26NGjxMXFYTKZWL9+Pd27d9e6PNFKmqecXq/HaDRSVlbmO12IhGhHdrudw4cPu4K2vLwck8nE4sWLvxspLnyW5qEL320xSOiKzqpppHhGRgbZ2dlERUWhKAq//vWvmx8pLnyWV4WuEJ3JhQsXXAMYDx06xMiRI1EUhTVr1lx7pLjwWRK6QnhI00jxpocUCgsLmTx58vWPFBc+SUJXiHZUU1PD3r17XdNuw8PDURSFDRs2MHLkSNk26IQkdIVoY82NFE9KSmq/keLCp3hN6BYWFmpdhhDXpWmkeNNpg/z8fBISEjw7Ulz4DK8J3ePHj2tdhhBuq6ur45NPPnEFrV6vJykpSduR4sIneEXoyvQI4QtKSkrIzs4mMzOT3NxcYmJiMJlMvPLKK9x0003yNJhwi1eEruzpCm/kcDg4ffq0azV78uRJxo8f73sjxYVXkdAV4gpNI8WbjnXV1NRgMplYsWIF48ePlyYy4oZJ6IpOr6Kigj179pCVlUV2djZ9+vRBURS2bt3KzTffLNsGok15ReiGhoZSV1dHXV2drCSER5w7d871NNiRI0cYM2YMiqLISHHR7rwidHU6HREREZSWlhIVFaV1OaIDstvtHDlyhKysLDIyMigpKWHy5MnMnz9fRooLj/KK0IXvxvZI6Iq20txI8aSkJB577DEZKS4041WhK/u64kYVFRW5ngY7ePAgw4cPx2QyyUhx4TUkdIVPczgcHD9+3HWsq6CggMTERCwWC08//TRhYWFalyjE93hN6MoDEsJddXV17Nu3z3UjrGmk+Pr16xk1ahQGg0HrEoW4Kq8JXVnpipY0jRTPzMxk3759MlJc+CyvCt2CggKtyxBeommkeNNDCnl5eUyYMIGpU6fy2GOPyUhx4bO8Kg7PmnAAAAibSURBVHS/+OILrcsQGmpoaPheExm73Y6iKKxevZqxY8fKGW7RIXhV6Mr2QudTVlZGdnY2WVlZ5OTkMGDAABRF4YUXXmDQoEGybSA6HAld4XH5+fmuhxSOHTtGXFyc60aYjBQXHZ1XhW5paanWZYh20NxIcUVRWLJkiYwUF52OV4WurHQ7jqqqKnJycsjMzCQ7O5sePXqgKApbtmxhyJAh8jSY6LS8JnSDg4NpbGyktraWwMBArcsR1+H8+fOup8EOHz7MqFGjMJlM3HffffTq1Uvr8oTwCl4Tulc2vZEuT77Bbrdz9OhR17bBxYsXmTx5MrfffjvPPfecNJERohleE7rw3RaDhK73ahop3vQ0mNFoxGQy8cgjjzBixAjZNhDiGrwydIV3+eabb8jKyiIrK4v9+/czdOhQkpKSWL58Of3799e6PCF8ioSu+BGHw8HJkydd2wZnzpwhISGB2267TUaKC3GDJHQF4Gwic+DAAdeNMIPB4JqkMGbMGPz8vOo/FSF8llf9SZLQ9aymkeIZGRns3buXmJgYFEWRkeJCtCOvC93Tp09rXUaH1dxI8fj4eBRF4ZFHHpGR4kJ4gNeF7qFDh7Quo0NpbGzk008/dQVtbW0tiqJw9913ExcXJ01khPAwrwtd2V64ceXl5a6nwfbs2UPfvn0xmUxs27aN2NhY2TYQQkMSuh1E00jxzMxMPv/8c8aOHYuiKKxdu1bOPQvhRbwqdGVkj/uaRoo3BW3TSPEFCxYQHx9PcHCw1iUKIZrhVaHbtNJ1OBzyI3AzqqqqyM3NdT2o0LVrV5KSknj88ccZNmyYPA0mhA/wqtANCgpCp9NRU1MjK7VvFRUVuVazn376KcOHD0dRFO655x769OmjdXlCiFbyqtCF71a7nTV0HQ4Hx44dczX5Pn/+PJMnT2bmzJls3bpVRooL4eO8NnR79+6tdSke0zRSPCMjg927dxMUFISiKDz88MMyUlyIDsYrQ7czTJD44Ujx2NhYFEXh97//PQMHDtS6PCFEO/HK0O2IJxgcDgd5eXmukeKnTp1i4sSJTJs2jccff5yIiAitSxRCeICEbjuqr6/n4MGDrhthDocDk8nEmjVrGDt2LP7+/lqXKITwMAndNtY0UjwzM5OPP/6YgQMHoigKL774Ij/5yU/kKJwQnZzXhW5ERAR5eXlal9Eq+fn5rtVs00jxpKQkfv7zn9OtWzetyxNCeBHvCl2Hg5719Vw4fhwOHICwMLjpJvCypiyNjY0cPnzYdayrsrISRVFYunQp8fHxMlhTCHFV2oduQwPs2gV/+Qvs3098RQWx5eXw8cfgcDhfj4mB+fPh9ttBo/aDlZWV3xsp3rNnTxRF4cknn5SR4kIIt+kcDsdVX4yLi3Ps37+//T59507YtAma9nBDQ6lpaOBcQQE/iYlx/p7dDjU1UFcHej0sXw7r14MHHp5oGimekZHBZ599xujRozGZTJhMJhkpLoS4Kp1Od8DhcMQ195o2K93KSvjlL8Fmg8BAuOK4lAHnj+8uej2EhDh/NTTAn/4EaWnw+utwyy1tWlbTSPGmY11NI8Xnzp3L888/LyPFhRA3zPOhW14OS5bAZ59BZKQzVK9gMBhobGzEAfzoPr+fn/M9hYUwbx789a8wduwNldPcSPGmSQoyUlwI0dY8G7p2O9x7Lxw5Al26QDPHp/Q6HTqdDrvdjuFqgWc0OlfLy5fD++9DdHSryrh48aKrU9eBAwe45ZZbUBRFRooLIdqdZ0P3v/8bcnOdq9UWzqs2rXavGroAoaFQWgoPPQT/+MePVsxX+uFI8bNnzzJx4kRSUlL49a9/LSPFhRAe47nQLSyErVudYXmNBwT8vg1drvXEltEIhw/D22/DggXfe6lppHhT0Pr5+ZGUlMTatWsZPXq0jBQXQmjCc8nz5pvOEwihodf8UoPBQGNDw7WvqdM5b8S9+irMn8/l0lLX02BNI8WTkpJ49dVXZaS4EMIreCZ0m04duHH3v6yxka1VVRwqL6dbYCAPRkWRcpVmMA6gTq+n9quveHX2bD4sK2PChAmYTCYZKS6E8EqeCd2vvnKetXWjAfe2Cxfw1+t5u3t3ikNCWHfmDLFBQcR8+5SXw+Ggqrqa8vJyKsrLcTgcRBkMLIqO5hcvvCAjxYUQXs0zoXv0qPPkwjVU2+2kl5fz+8hIAh0ORoeEkBQezvslJdwVHExFeTkVlZUE+PsTFh5Ov379CAwKQldRQWRRkdc9LiyEED/kmdDNy3Pu515Dfl0dBiA6MJDKykqKL12iS0UFn9TUMKdLF8LDw+nRsyf+P7wJFhDgXE0LIYSX80zoVlW1eKTL9WV2O6F6PcEhIRQXF6M3GOgZFkZAQAADWjo/q9e7FepCCKE1z4RuSIhb2wshej2VdjtBgYEMGjQIAHtxMWFXPhbcHLtdthaEED7BM8+4/uQnboXigIAAGnFuMzQ5UVvruol2VXV1zs8QQggv55nQHTr0mg9EAATr9UwLD+cPFy9SbbdzqKqKjPJyLNeaH1ZbCxMntlGxQgjRfjy30g0NdYbjNTzSqxe1djvJJ06w6dw5Nvbq1fJK1+FwNsJJTGzDgoUQon14Zk/XYIC774bf/tb5BFkLjAYDL7Sm6Ux1NfTuDXHNtq4UQgiv4rm+hfPnO/d12/KUgcPhvN6DD7p1OkIIIbTmuaTq0QMef9zZkrGFaRWtUloKo0fDHXe0zfWEEKKdeXZ5uGABJCQ4x/PcaPBWVDhH9rz4oqxyhRA+w7Nppdc7x+yMGuUMXjfO7jarrMx58+y//gsGDGjbGoUQoh15fokYGuoMy5kzndsDVVXuv7ehwRnWffrA//6vM7yFEMKHtDgNWKfTXQROe64cIYToEAY6HI6o5l5oMXSFEEK0LbkDJYQQHiShK4QQHiShK4QQHiShK4QQHiShK4QQHvT/AXZyCZ93hy7iAAAAAElFTkSuQmCC\n",
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CRkYGQ4cOJSMjo/iaiYkJw4cPZ/Xq1Wzbto233nqLP/74w4DRClE7SdKtYezt7VmwYAFvvvkmwcHBqNXqB3q5Hh4erFu3jh49ehAUFERiYqIM0BGiEknSrYEUCgUDBgwgPj6erVu3Mn78eHJycoqvK5VKVCoVSUlJ7Nu3jyFDhhQfuBBCPF2SdGuwxo0bEx8fT8uWLVGpVOzateuB666urqxYsQJvb2/efvttVq5cSX5+vmGCFaKWkKRbwxkbG/PWW28RGxvLokWLmD17Nnfu3Cm+rlAo6NevHykpKZw+fRp/f3+OHDliwIiFqNkk6dYSbdq0ITU1FY1Gg7+/Pz///PMD1x0cHJg/fz4jR45kypQpLFiw4IHkLISoGJJ0axFLS0siIyMZO3YskyZNYvXq1Q+8RFMoFLz88suo1Wpu3LiBr68v//nPfwwYsRA1jwy8qaWysrKYOXMmt27dYtasWSV+e23v3r3MmTOHDh06MGHCBGxtbQ0QqRDVjwy8EQ9xcHBg6dKlvPbaawQFBfHxxx8/dEy4S5cuqNVqzM3N8fHx4ZtvvjFQtELUHFLpCs6cOUNERAQuLi5ERERQt27dh9b897//ZdasWXh6ejJ16lTs7e0NEKkQ1YNUuqJMHh4eJCQk0KRJE/z8/Ni7d+9Da55//nnS0tJo2LAhvr6+bNu2TQboCPEYpNIVD/jpp59499136d69O+PGjcPc3PyhNSdOnCAqKop69eoRHh6Oi4uLASIVouqSSlforV27dqSmpnL9+nUCAwM5ceLEQ2uaN29OYmIizz//PAEBAajVahmgI4SepNIVpfriiy9YsGABAQEBBAYGPvBpoHvOnj1LVFQUSqWSGTNm0KRJEwNEKkTVIpWueCy9e/cmMTGR77//nrfeeotLly49tMbNzY21a9fyyiuvMGzYMBISEigsLDRAtEJUD5J0RZlcXFyIi4ujW7duDB48mM8///yhF2hKpRIfHx+SkpI4cOAAQ4YM4eTJkwaKWIiqTdoLQm8nT54kIiKCv/3tb0ybNq3EwxJarZatW7eydOlS3njjDUaMGIGpqakBohXCcKS9ICrEM888Q3JyMnXr1kWlUlHSP8gKhYLXX3+dtLQ0zp49i0ql4vDhwwaIVoiqSSpd8Vj27t3LrFmz6N27N6NGjSq1mt25cycxMTH07NmTMWPGYGlpWcmRClH5pNIVFa5Lly6kpqZy7tw5hgwZwunTp0tc17NnT9RqNbm5ufj4+PDDDz9UcqRCVC1S6YonotVq+fTTT1m6dCnDhw/Hx8enxK1lAPv27eP999+nXbt2hIaGygAdUWNJpSuemntD0BMSEti+fTtjx44lKyurxLWdOnUiPT0dKysrvL292blzZyVHK4ThSdIVFaJhw4asXbuWtm3b4u/vX2pCtbS0ZMqUKURHR7NixQqmTp1KdnZ2JUcrhOFI0hUVxsjIiODgYBYuXMiyZcuYOXMmt2/fLnHtc889R2pqKm5ubqhUKj799FMZoCNqBUm6osK1bt2ajRs3YmxsXOaWMVNTU0aPHs2KFStIS0tjzJgxXLx4sZKjFaJyyYs08VTt3r2b999/nzfeeIPg4GCMjY1LXFdUVERycjKJiYkEBwfj7e395wu5ggI4fRquXNH9t60tNGsGJUxAE6IqKOtFmiRd8dTl5OQwc+ZMrl27xuzZs2ncuHGpa3///XdmzZqF8d27zOzQAacvvoCTJ8HYGBQK3SKtVpeI3d3Bzw/eeANKGLwuhKFI0hUGp9Vq2bx5M6tWrWL06NG88cYbKO4l0ftpNGg2bODmjBncvHIFazs77JydURgZPbSOvDzIzwelEkaOhLFjQY4ciypAkq6oMjIyMpgxYwaOjo5EREQ8+Nmfy5dh1Cg4dAisrMhXKMjMzKSwsBAXFxcsSmsnFBbCzZvg5garV0PTppXyuwhRGtmnK6oMd3d31q9fj4eHB35+fnz33Xe6Cxcv6toEhw9DnTpgaoqpiQmNGjWinr095/74g/9dvoympCLB2Fh3zx9/wIABUMLgdSGqCql0hcEcPHhQ92mg9u0J3bEDo0uXwM6uxLWFhYVk/u9/5OXl4eriUvoMh5s3wcoKvvwSHByeYvRClE4qXVEleXl5kZqaitc333D5p5/INTMrda2xsTENGzTA0dGR8xcucCkzk6KSPhFkYwM3bsC0aboXbkJUMZJ0hUFZ//47L124gKWzM+f++IPs7GzKSpW2NjY09fBAq9Vy5swZbt269fAiOzvYvRvkmLGogiTpCsNauxY0Guzq1sXd3Z3bd+7w+9mz5BcUlHqLkZERri4uuLq4cCkzkwsXL1JYVPTnAoUCjIxg+fJK+AWEKB9JusJwrl2Dbdt0hx0AExMTGjdujI2tLRkZGVy7fr246lVfuUJgRgadT5zgvf8/tWZlZUVTDw+MjIw4c+YMN27c+LNKtraGo0fht98q/dcSoiySdIXhHD6s22N73x5cBVDP3p4mTZqQk5PDhfPnKSwqor6xMcPr16ffX160KZVKnJ2caNiwIVnZ2Zw/f56CwkJdtavVwoEDlfxLCVE2SbrCcI4ehbt3S7xkbmaGu7s7xiYmZJw5QwcjI/5hY4PdXw9J/D9LCwvc3d0xMzMj48wZrl27pqt6JemKKqbkg/BCVIbjx3V7bEuhVChwdnLCxtqaixcvYmNri7akU2z3rXd0cMDWxoaLly5xR6ul7pEjWDyN2IV4TFLpCsPJy/tznkIZrKys8PDwoKioiJycHPLz88tcb25ujru7OxYWFvxy8CApKSloStpeJoQBSNIVhmNpqfdeWiMjIxq4umJtbc3NW7d0vdsydjgogLp2drT08mLXrl0MHz6cM2fOVFDgQjw+SbrCcFq3hvu3eunB3MwM+7p1MTMz40xGBtnZ2SUfDQa4exfL9u2Ji4ujb9++hISEsHbt2jKTtRBPmyRdYTitWuk9FaxIqyVfo0EDaBUK7OrVo7GbG7m5uaUfklAqoV07lEolb775Jhs3buTnn38mMDCQY8eOVezvIoSe5EWaMJznntP1dAsLy3yhBrAuO5vV931L7bPr1wmpX5+QRo24desWmZmZmJmb4+TkhKmJiW70o1YLHTsW3+Pk5MTixYv54osvmDBhQnH1ay7D0EUlkoE3wrDCwyE9/YmHkGu0Wq7k5JBz5Qr29vbUMzZG2bEjbNxY4vorV64QGxvL8ePHmTFjBl5eXk/084W4nwy8EVXX8OG6wxGFhU/0GKVCQf369XF3d+dubi7/u3SJQ926lbre3t6eOXPmMHHiRCIiIpg7d26pH9EUoiJJ0hWG1bQpjB6tG8lYAVPBTE1MaGhtjaZfP6K++oqJEydy4cKFUtd3796d9PR0NBoN3t7ef873FeIpkfaCMLz8fBg4EI4d000I02PvbqmuXwdHR/j8c/LNzUlJSSEpKQlvb2+GDh2KWRnjIw8cOMDs2bNp06YNkyZNok6dOo8fh6jVpL0gqjZTU9iwQVf1XrumewlWXlotXL0K9vaQlga2tpiamjJ06FBSUlLIyMhg0KBB7N69m9IKjb///e+kpaVhb2+Pt7c327dvL3WtEI9LKl1Rddy4AVOmwNdf6z6vbqHnAd67d+HOHfDygqVLwcWlxGX79+8nJiYGV1dXJk+eXOZXiY8ePUpUVBQNGzZk2rRpODo6Ps5vJGopqXRF9WBrC3Fxujm4Vla6JHz9uq798NfioKDgz+sKBbz3nm4XRCkJF6BDhw6kpqbSvn17goKC+OCDD8jNzS1xbevWrdm4cSPNmzfHz8+Pjz76SKpeUSGk0hVVU1ER7Nmj2/J18KAuwd7by1tUBGZm0LYt+PnByy+X+9Prly9fZvHixRw5coTQ0FB69OhR8ifhgd9++42oqCgsLS2JiIigYcOGT/rbiRpOPsEuqr/sbF3PVqvVVcROTk/2wu3//fjjj8TExODo6MiUKVNo0qRJies0Gg0pKSmsX7+eYcOGoVKpUCrlfxRFySTpClGGwsJC0tPTiY+P51//+hfDhw8v9WvD586dY/bs2eTl5REZGUnTpk0rOVpRHUhPV4gyGBsb4+/vT3p6OpcvX2bQoEF8/fXXJfZwGzVqxMqVK+nfvz8jR45k9erVMkBHlItUukL8xcGDB4mJicHe3p4pU6bg7u5e4rrLly8zd+5cLl68SGRkJK1atarkSEVVJe0FIcqpqKgItVrN2rVr6devH8HBwSW2HLRaLV999RWxsbG89tprjBo1SgboCGkvCFFeRkZGqFQq1Go1V65cYeDAgSUellAoFLz66quo1WpycnLw8fFBChVRFql0hdDD4cOHiY6OxtbWlqlTp+Lh4VHium+//Za5c+fStWtXxo8fj7W1dSVHKqoCqXSFeELPPfccSUlJ9OzZk5CQEBYtWlTiVLIXXngBtVqNQqHA29ubPXv2GCBaUZVJ0hVCT0ZGRnh7e6NWq7l58yYDBw7ks88+e6jlYG1tTXh4OLNmzWLhwoWEh4dz9epVA0UtqhpJukKUk729PZGRkURHR7Nx40ZCQkL49ddfH1rXrl070tLScHJywsfHhy+++EKOEgvp6QrxJDQaDR9++CGrVq2id+/ejBw5ssQ+7rFjx4iKisLZ2Znp06fj5ORkgGhFZZGerhBPiVKpZODAgWzatInc3FzefPNNtm7diuYv4ylbtmxJUlISrVq1wt/fnw8//PChNaJ2kEpXiAr0yy+/EB0djbGxMdOmTcPT0/OhNWfOnCEqKgpTU1MiIiLKHDEpqiepdIWoJK1atSIhIYG+ffsyZswYYmJiuHnz5gNrPDw8iI+P5x//+AdBQUEkJiZSVFRkoIhFZZOkK0QFUyqVDBgwgE2bNlFYWMibb77JJ5988kA7QalU4ufnR2JiIvv27SMoKKjEl3Gi5pH2ghBP2bFjx4iJiUGhUBAWFkbz5s0fuK7Vavnkk09YtmwZAwcOZNiwYZiWcz6wqFqkvSCEAbVs2bJ4bOS4ceOYN28eN27cKL6uUCjo378/qamp/PrrrwQEBPDzzz8bMGLxNEnSFaISKJVK+vfvz+bNm4t3PHz00UcPtBwcHByIjY0lODiYyZMns3DhwlI/JySqL0m6QlSie7Mbli1bxqeffsrQoUM5duxY8XWFQsErr7yCWq3m2rVr+Pj4sH//fgNGLCqa9HSFMBCNRsO2bdtYvnw53bt35+2336ZOnToPrPn++++ZM2cOnTp1YsKECdjY2BgoWlEe0tMVogpSKpW8/vrrbN68GTMzMwYNGsSWLVseaDl07doVtVqNqakp3t7e7Nq1y3ABiwohla4QVcSpU6eIiYnh7t27hIWF0bp16weuHzx4kNmzZ+Pp6cnUqVOxt7c3UKTiUaTSFaIa8PT0ZM2aNahUKiZPnkxUVNQD08m8vLxITU2lQYMG+Pr6ljjhTFR9knSFqEIUCgV9+vRhy5YtWFtbF4+SvNdyMDMzY+zYsSxZsoTk5GTGjx9PZmamgaMW5SFJV4gqyMrKitDQUOLi4vj6668JCAjgyJEjxddbtGhBYmIibdu2JSAggE2bNskAnWpCerpCVHFarZbt27ezZMkSOnTowLhx4x7o52ZkZDBr1iyMjIyYMWOGDNCpAqSnK0Q1plAo6NWrF5s3b6Zu3bp4e3uTlpZWPCTH3d2dtWvX8tJLLxEUFMSGDRtkgE4VJklXiGrC0tKS8ePHs3r1anbv3k1AQAD//e9/Ad32M19fX5KSkti/fz9Dhgzh1KlTBo5YlETaC0JUQ1qtlq+//ppFixbRvn17xo0bR/369Yuvbd26laVLl/LGG28wYsQIGaBTyaS9IEQNc++48ObNm3FwcMDHx4eUlBQKCwtRKBS8/vrrpKamkpGRgZ+f3wMv4YRhSaUrRA1w9uxZ5s+fT3Z2NmFhYXh5eRVf27lzJzExMbz00ku8/fbbWFpaGjDS2kEqXSFqODc3N5YvX87IkSOJjIzknXfeISsrC4CePXuiVqu5ffs2vr6+7Nu3z8DR1m5S6QpRw+Tm5rJ+/Xq2bNnC0KFDUalUGBsbA/DDDz8wZ84c2rdvz8SJE7G1tTVwtDWTVLpC1CIWFhaMHj2a9evXc+DAAVQqFQcOHACgc+fOpKenY2FhgY+PDyp5M2IAAAniSURBVDt37jRwtLWPVLpC1GBarZbdu3ezYMECWrduzcSJE3F0dATg0KFDzJo1i6ZNmxIWFka9evUMHG3NIZWuELWUQqHgH//4B5s2baJJkyaoVCo2bNhAQUEBbdu2JTU1tfjvt27dKgN0KoFUukLUIufPnyc2NpZz584xdepUOnbsCMDJkyeZOXMm9vb2hIeH4+rqauBIq7eyKl1JukLUQnv27CE2NpbmzZsTGhqKs7MzhYWFJCUlkZycTEhICIMGDUKplP8ZfhzSXhBCPKB79+5s2rSJZs2a4e/vT3x8PBqNhqCgINatW8f27dsJDg7m7Nmzhg61xpGkK0QtZWZmRkhICElJSRw9ehRfX1/27t2Lm5sba9asoVevXgwfPpz4+HgKCwsNHW6NIe0FIQSg+wjm/PnzadasGaGhobi6unLp0iXmzJlDTk4O7777Ls8884yhw6wWpL0ghHikex/BbNGiBYGBgaxdu5Z69eqxdOlS/Pz8GDNmDMuXLyc/P9/QoVZrknSFEMVMTU0ZPnw4ycnJnDx5Em9vb77//nv69u1LWloa586dw9fXl0OHDhk61GpL2gtCiFL98MMPzJ8/Hzc3NyZNmkSDBg3YuXMn8+fPp0ePHowZM0YG6JRA2gtCiMfSuXNn0tLSePbZZxk8eDCrV6+ma9eupKenk5eXh7e3N3v37jV0mNWKVLpCCL1kZmayaNEijh8/zuTJk3nhhRfYv38/77//Ps8//zyhoaHY2dkZOswqQSpdIcQTc3Z2Jjo6moiICJYsWcKECRNwdXUlLS0NGxsbfHx82LFjhxwlfgSpdIUQ5VZQUEBqaiobNmxg4MCBBAUFcerUKaKionB3dycsLKz480G1kVS6QogKZWJiwuDBg0lNTeXcuXMMGjSInJwcNm7ciLu7OyqVik8++USq3hJIpSuEeGI//vgjMTExODk5MWXKFPLy8oiKisLW1paIiIhaN0BHKl0hxFPVvn17UlJS6NSpE0FBQXz11VfExcXRqVMnAgMDSU1NRaPRGDrMKkGSrhCiQhgbG+Pv7096ejqZmZn4+PjQoEED4uPj2blzJyNGjODMmTOGDtPgpL0ghHgqDh48SHR0NPXq1WPy5MkcPHiQuLg4VCoVQ4YMKf5uW00k7QUhRKXz8vJi48aNvPDCC4SEhHDx4kVWr17N4cOHCQwM5Pjx44YO0SAk6QohnhpjY2NUKhXp6enk5OQwZswY/vnPfxIQEMD48eNZunQpd+/eNXSYlUraC0KISnPo0CFiYmKws7MjODiYTZs2cfLkSSIiIvDy8jJ0eBVG2gtCiCqhbdu2JCUl0aNHD6ZOnYqTkxMhISFEREQwb948bt++begQnzpJukKISmVkZIS3tzdqtZobN26wZMkSRowYQX5+fvEoyZpM2gtCCIM6cuQI0dHRWFlZ0atXLzZs2EDbtm0JDQ2lTp06hg7vsUh7QQhRZbVp04akpCReffVV4uLi6Nq1KxYWFvj4+LB9+/Yad5RYkq4QwuCUSiUDBw5ErVZTUFDA7t276d+/P2vWrGHSpElkZWUZOsQKI0lXCFFl1K1bl4iICBYsWMC+ffuwtramTp06qFQqPv744xpR9UrSFUJUOa1atSIhIYHXX3+d7777jnbt2pGens7o0aO5cOGCocN7IpJ0hRBVklKpZMCAAWzatAk7OztycnKwsLAgMDCQlJSUajtAR3YvCCGqhWPHjhEdHU1eXh5arRZLS0siIyPx8PAwdGgPkd0LQohqr2XLlqxfvx4/Pz+uXr1KUVERw4YNY82aNRQUFBg6PL1J0hVCVBtKpZL+/fuzZcsWWrduDcAnn3yCv78/x44dM3B0+pH2ghCi2jp58iTz5s3j/Pnz3L59G29vb9566y3Mzc31f0h+PuzaBTt2wI8/wh9/QEEBmJhA48bQvj307Ak9eoCpqV6PLKu9IElXCFGtaTQatm3bxqJFi9BoNFhZWREVFUW7du3KvjE/H9atg1Wr4M4dKCoCc3MwMwOFArRauHsX8vLAyAgsLSE4WPfnEclXkq4Qosa7efMmcXFxqNVqioqKGDBgABMmTMDa2vrhxcePw9ixkJGhS6ZmZo/+AXfv6pKzmxssWwYtW5a6VF6kCSFqPBsbG6ZMmcLGjRt59tln2bJlC6+99hrffvvtgwu/+w4GDNC1EerU0S/hgm5dnTpw7pzu/r8+V0+SdIUQNYqnpycJCQm8//77FBYWEhISQmhoKFevXoWDB2H4cFAqwdZW10YoD4VCd5+REYwYoesBl5O0F4QQNdatW7dYsWIF8fHx1DUx4YvCQuwARUkth/I/XJeAd+wAG5sHLkl7QQhRK1lbWxMWFsa2bduYXFTEtdOnycjKoqCwsCIeDjk5MHduuW6TpCuEqPGa2dnxZmEhdRo3JvfOHU4cP052Tg5PPD7H1hY2b4bLl/W+RZKuEKLm27wZhVZLnbp1ad6iBfb29lw4f55Tp05xNz//gaU3ioqYfP483U6coO9vv/HF9eulP9fICDQa2LRJ71Ak6Qohar6PPireW2ukVNKgQQOead4cgBPHj3Px0qXiqndeZiYmCgXbPT2Z7erK3MxMzpT1xWIzM/jwQ71DkaQrhKjZ8vPhzBndwYf7mJuZ4enpScNGjcjOyuLYsWNk37zJzps3GeXggKVSSVtLS160sWFbWdWumRmcPavbx6sHSbpCiJrt7FkwNtZtE/sLBVDP3p5WrVtjZWXFt7/9RsHduzQ0Ni5e8zczs7IrXaVSd2Q4I0OvcCTpCiFqtjt3Hrkf10ipxK1JE5yaNMFMo+HMfQnUWqnk9qNm9yoUup+jB0m6QoiazchI76X1LC1RWFjg7u5e/He3NRqsSqiSH/fnSNIVQtRsTk66qWF6fF+tsakpRcCF+/bxnrp7F4+yjgprtbrnOznpFY4kXSFEzebgoDvIoMeBCAulkp42NsRlZZGr0XD4zh1237zJP+3sSr+psFA3NEeSrhBCoOu3duoEt2/rtXyaszN3NRpeOXWK8AsXmO7sXHale/s2dOyo9xwH40cvEUKIam7wYN2MBK32kcnR1siIBY0a6f9shQKGDtV7uVS6Qoiar1MncHHRu9rV2+3burZC58563yJJVwhR8ymVEBOje+FVVFQxz9RodM+LiSlxD3CpoVTMTxdCiCquUyfw84MbN/TayVAmrRauXYNBg6BLl3LdKklXCFF7zJgBXbvqEuajDjyURqPR3d+lC8ycWe7bJekKIWoPU1NYuxb69IHr1yE3t3z35+bq7uvdW/dRSz2/Dny/Mr8coVAosoDfy/1UIYSo3ZpotVqHki6UmXSFEEJULGkvCCFEJZKkK4QQlUiSrhBCVCJJukIIUYkk6QohRCX6P5f9PVyKq8LjAAAAAElFTkSuQmCC\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -977,7 +1054,7 @@
     "ry = RY(qubitOp.num_qubits, depth=5, entanglement='linear')\n",
     "vqe = VQE(qubitOp, ry, spsa)\n",
     "\n",
-    "backend = BasicAer.get_backend('qasm_simulator')\n",
+    "backend = Aer.get_backend('qasm_simulator')\n",
     "quantum_instance = QuantumInstance(backend, shots=1024, seed_simulator=seed, seed_transpiler=seed)\n",
     "\n",
     "result = vqe.run(quantum_instance)\n",
@@ -1009,23 +1086,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
+   "execution_count": 23,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T18:21:15.324815Z",
+     "start_time": "2019-12-12T18:21:15.110374Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "energy: -2889.5\n",
-      "tsp objective: 79.0\n",
+      "energy: -5627.0\n",
+      "tsp objective: 154.0\n",
       "feasible: True\n",
-      "solution: [1, 2, 0]\n",
-      "solution objective: 79.0\n"
+      "solution: [0, 1, 2]\n",
+      "solution objective: 154.0\n"
      ]
     },
     {
      "data": {
-      "image/png": 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uIKErNGc0Grn99tu5/fbbKSgowGazsXnzZux2u6sBT//+/bUuU3RgHg1dmSAhvEmfPn1YuXIld999N0ePHsVqtbJy5Ur69u3rasATGRmpdZmig9FkpSuEN9HpdNxyyy3ccsstrgY8qamp/Md//Afjxo1zNeCRexGiLUjoCnEFPz8/EhMTSUxMpLKykvT0dN555x22bt3K1KlTsVgsjB49WhrwiOsmoSvEVYSGhjJz5kxmzpxJUVERaWlpPPfcc1RUVGA2mzGbzdKAR7Sax0M3Pz/fkx8pRJvo0aMHy5YtY9myZZw8eRKr1coDDzxAt27dUFWVGTNm0K1bN63LFD7Aoz8jyUpXdASDBw9m3bp1pKamsnbtWk6cOMEdd9zBT3/6U2w2G9XV1VqXKLyYx1e6Mj1CdBR6vZ74+Hji4+OpqakhIyOD1NRUnn32WRRFQVVV4uPjZf9XfI+sdIVoA0FBQcyYMYNXXnmFd955h6FDh/Laa6+hqiq//e1vOXHihDTgEYDcSBOizXXt2pWFCxeycOFCvv76a6xWKw8//DDBwcGuBzB69uypdZlCIx7rvQBQXV3NrbfeSnZ2dptdUwhfYLfbOXToEFarlX/+858MHjwYi8XCtGnTCAsL07o80ca8ouENOJveJCYmkp6eTlBQUJtdVwhfUldXx+7du7Farezbt49JkyZhNpuZNGkSfn4e/eFTtJOWQteje7pXNr0RorMKCAhg2rRp/OY3v+H9998nLi6OP//5z5jNZp577jmOHDki+78dmMf/t9oUur169fL0RwvhdYxGI3PnzmXu3LmcO3cOm83GE088AeB6AKNfv34aVynakmahK4T4vr59+3LPPfewcuVKvvjiC1JTU1mxYgX9+/d3NeCJiIjQukxxgyR0hfAyOp2OYcOGMWzYMNavX09OTg42m41XX32V8ePHo6oqkydPlgY8PkqT0JUHJIRwj5+fHyaTCZPJREVFBenp6bz99ts89dRTTJ8+HbPZLA14fIysdIXwEWFhYcyaNYtZs2ZRWFhIWloa27Zto7q6GrPZjKqqREdHa12muAZNQvfUqVOe/lghOpSePXuyfPny7zXgWbNmDVFRUa4GPF27dtW6TNEMWekK4cN0Oh2xsbHExsaydu1a9u3bR2pqKq+//jojR45EVVWmTJki5+K9iISuEB2EXq9nwoQJTJgwgerqanbt2oXVanU14LFYLMTFxcn+r8YkdIXogIKDg13nfIuLi9mxYwevvPIKxcXFpKSkYDabiY2N1brMTklCV4gOrlu3bixatIhFixaRl5eHzWZj/fr1hIWFoaoqKSkpMqHbgzzaewGgtraWKVOmsGfPHnQ6XZteWwjhHrvdzqefforVaiU9PZ2bb74ZVVWZNm0aoaGhWpfn87ym4U0Tk8nEjh07CAkJafNrCyFap66ujqysLFJTUzlw4ACJiYmoqsrEiROlAc91ail0Nfkn2rTFIKErhPYCAgKYPn0606dPp6SkhJ07d/LGG2+wZcsWkpOTsVgsDB06VH4ybSOahm6fPn20+HghxFVERkYyb9485s2bx9mzZ7FarWzatAm9Xo/FYsFsNsuf2xukaegKIbxXv379WL16NatWreLIkSPYbDaWLVtGdHQ0qqpy6623YjQatS7T50joCiFapNPpGDFiBCNGjOChhx4iJycHq9XKyy+/THx8PKqqkpiYKA143KRJ6EZEREjoCuGD/P39URQFRVEoLy8nPT2dN99809WAR1VVRo0aJfu/LZCVrhDiuoSHhzN79mxmz57NhQsXSEtLY+vWrdTU1Lga8AwcOFDrMr2OJs8DSugK0bH06tWLu+66i7feeovnn3+e6upqVq9ezbJly3jzzTe5dOmS1iV6DVnpCiHajE6n4+abb+bmm29m3bp17N27F5vNxh/+8AdGjRqFxWJBUZRO3YBHQlcI0S4MBgMJCQkkJCRQVVXFrl272L59O8888wxTpkzBbDZ3ygY8ErpCiHYXEhKCqqqoqso333zDjh07ePnll7l8+TIpKSmoqsqgQYO0LtMjNAtdGdkjROfUvXt3Fi9ezOLFi8nLy8NqtbJu3TqMRqOrAbsmDXguXoSjR+H8eWhshKAgGDQIYmOdf99GNOm9UF9fj8lkIicnR46WCCGw2+0cPHiQ1NRUdu3axZAhQ1wNeNq1XUBJCbz7Lvznf0JhIfj5QUMDOByg14PBAPX1MHEirFoFJpPz967B6xreACQlJZGamkpYWFi7XF8I4Ztqa2vJzMzEZrPxySefkJiYiMViYcKECRjcCDy32O3w97/DU085QzUw0LmabW4RaLdDebnz76Oj4eWX4ZZbWry8V4burFmzeO211+jXr1+7XF8I4fsuX77Mzp07sVqtFBQUMGPGDMxm84014CkthXvvhX37IDQU3H2SzuFwhq/DAb/4hXPle5UavK7LGHx3M01CVwhxNV26dGH+/PnMnz+f/Px8bDYbGzduxN/f39WAvVUNeEpLYd48+OoriIy8amg2S6cDo9G5/fDss1BRAevXt/p70uyshpxgEEK0xoABA7j33nt57733eOKJJygqKmLp0qWsWrWKd999l7KyspYvYLfDmjXXF7hX8vNzhu/vfufcD24lCV0hhE/R6XSMHDmSRx55hLS0NJYsWUJOTg4zZ85kw4YN7Nq1i/r6+h+/8a23YO/eGwvcJgYDBAfDY4/BhQutequErhDCZ/n7+5OUlMRzzz3HBx98QEJCAn/7299ISUnhmWee4fDhwzgcDue2wpNPQkjIjQduk6AgqKmBp59u1ds039MVQoi2EB4ezpw5c5gzZw4FBQWkpaWxZcsW6uvrWdelC6aqKvy7d2/bDzUaYccO5xnfqCi33qJp6J4/f16rjxdCdGB9+vTh7rvvZsWKFRw7doxwi4VzFy/iqKggIiICo9GI3w+On9XZ7Wy7cIG9VVWUNTbSz9+fB3v0YFJLx1oNBueDFO+95zzN4AbZXhBCdFg6nY6hPXvST6dj4JAhRHXvTnVVFV99+SVnzpyhrKwM+7fHZhuBXv7+/HHAAHbFxnJfVBSPnDtHQV1dyx/i5we7drldk2wvCCE6tmPHwN8fnU5HWFgYYWFhNNrtlJeXc7mkhPMXLhAeHk5ERASroqJo2vE1hYfTx9+fYzU19GnpLG9QEHz2mfP8rhv7xRK6QoiOramXwhUMej2RERFERkRQ39BAWVkZhYWFNDY0YDQaiYyMpNJgIL+ujpjAwJav7+cHly5Bba1bPRokdIUQHVtjo3MVehX+fn5ERERgMBgoKSmh6OJFHHo9W6qr+beICKKvFbo6nbNPQ0ODW+VoFrpGo5HS0lLsdnun66cphPCgoCBnKF7BAdTV1lJeUUFFeTk1tbWEhYYSGRlJn7592VxYiB+woVeva1/f4XD+crMTmWah6+fnR2hoKBUVFTLGWQjRfgYNAr0eh8NBVVWVK2gdDgdh4eF0j4oiJCQEvU6Hw+Fgy/nzFDc08Er//vi5c6a3thb69XNuM7hBs9CF77YYJHSFEO2hrKyMj/PyGFdYSHFDA/4BAYSFh9OvXz8Cg4L4YaQ+c+ECp+rqeG3AAALd/Qm8pgbGjnW7Jq8I3QEDBmhZhhCiAzlz5gxZWVlkZmbyxRdfMG7cOGLHjiUmLw+/bt2u+r7z9fW8U1JCgE7HjJMnXb+/qVcvzBERV/9AnQ4sFrfr84rQFUKI62W32/nss8/IyMggKyuL0tJSFEVh0aJFxMfHO4dg7tkDy5e3eKyrt78/+4cObd2H19ZCeDhMner2WyR0hRA+p6qqio8//pjMzEx2795NVFQUJpOJzZs3M3To0B/fnJ84EQYPhpMnoaVVa2s4HFBVBU884fZ+LkjoCiF8RGFhIZmZmWRmZnLo0CFGjBhBUlIS9957L7179275zXo9vPQS/Nu/OSdF+PvfeEGlpTByJCxd2qq3aRq6ERERErpCiGbZ7XaOHTvmCtrCwkISExP593//d7Zt20ZoaGjrLhgb62zFuHmzs1FNK1anP1Je7pw68fLLPzqOdi2ar3Tz8/O1LEEI4UVqa2vZu3cvmZmZZGVlERoaSlJSEr/4xS8YOXLkjc9IW7bMOfHhhRec/XBbO+W3qU1kaKhzxtp1HALQPHRlpStE51ZcXOw6bbB//36GDBmCoigsXbq0fU423X8/DBwIGzc6pwGHh7s14ZeaGqiuhlGjnFsV11mbhK4QwqMcDgdffvklWVlZZGRkkJ+fz8SJE0lOTmbz5s2eObdvsUB8PGzbBu+/73xU2GBwrnz9/Z0nHOx25+mEmhrna0YjbNgAS5a0ekvhShK6Qoh2V19fzyeffOI61qXT6VAUhQceeIAxY8bg3xY3tlorKsq5zfDoo7B9O2RkwOHDUFzsDF2dzjlyfdw4SEmBpKQb2wf+loSuEKJdlJaWkp2dTUZGBrm5udx0000oisJLL71ETEzM9Y9Qb2tdu8Jddzl/gfN0Q2OjczR7O/SF0TR0jUYjlZWV0vRGiA7i9OnTrtXs8ePHiY+PR1EUfvnLX9K1a1ety3OPv3/bHCm7Ck1DV6/XExYWRllZGZGRkVqWIoS4Do2NjRw6dMh1rKu6uhqTycRdd91FXFwcgddqi9gJaRq68N0Wg4SuEL6hoqKCnJwcMjMzyc7Opnfv3iQlJfH0008zZMgQ79k28FJeE7pCCO9VUFDgWs0eOXKE0aNHk5SUxE9/+lN69OihdXk+RfPQlafShPA+drudzz//3BW0ly5dwmQyMW/ePH7zm98QEhKidYk+S/PQlZWuEN6hurqa3NxcVxOZyMhIFEXh0UcfZfjw4XKzu41I6ArRiRUVFbF7924yMjI4ePAgw4YNQ1EUVq5cSd++fbUur0PyitC9dOmS1mUI0Sk4HA5OnDjhOtZ17tw5Jk2ahMVi4amnniI8PFzrEjs8rwjdvLw8rcsQosOqq6tj//79rqANDAxEURR+9rOfMWrUKPza4Ckr4T7N/2lHRkZSWlqqdRlCdCiXLl1i9+7dZGVlsXfvXgYPHoyiKLz22msMHDhQjnVpyCtCV/Z0hbgxDoeDU6dOuU4b5OXlER8fz5QpU3j00UflHLwXkdAVwkc1NDRw8OBBV9A2NDSQlJTEqlWrGDduHAEBAVqXKJohoSuEDykrK2PPnj1kZmaSk5ND//79SUpK4vnnn2fw4MGybeADNA/dsLAwqqqqaGhokA19IZpx5swZ12r26NGjxMXFYTKZWL9+Pd27d9e6PNFKmqecXq/HaDRSVlbmO12IhGhHdrudw4cPu4K2vLwck8nE4sWLvxspLnyW5qEL320xSOiKzqpppHhGRgbZ2dlERUWhKAq//vWvmx8pLnyWV4WuEJ3JhQsXXAMYDx06xMiRI1EUhTVr1lx7pLjwWRK6QnhI00jxpocUCgsLmTx58vWPFBc+SUJXiHZUU1PD3r17XdNuw8PDURSFDRs2MHLkSNk26IQkdIVoY82NFE9KSmq/keLCp3hN6BYWFmpdhhDXpWmkeNNpg/z8fBISEjw7Ulz4DK8J3ePHj2tdhhBuq6ur45NPPnEFrV6vJykpSduR4sIneEXoyvQI4QtKSkrIzs4mMzOT3NxcYmJiMJlMvPLKK9x0003yNJhwi1eEruzpCm/kcDg4ffq0azV78uRJxo8f73sjxYVXkdAV4gpNI8WbjnXV1NRgMplYsWIF48ePlyYy4oZJ6IpOr6Kigj179pCVlUV2djZ9+vRBURS2bt3KzTffLNsGok15ReiGhoZSV1dHXV2drCSER5w7d871NNiRI0cYM2YMiqLISHHR7rwidHU6HREREZSWlhIVFaV1OaIDstvtHDlyhKysLDIyMigpKWHy5MnMnz9fRooLj/KK0IXvxvZI6Iq20txI8aSkJB577DEZKS4041WhK/u64kYVFRW5ngY7ePAgw4cPx2QyyUhx4TUkdIVPczgcHD9+3HWsq6CggMTERCwWC08//TRhYWFalyjE93hN6MoDEsJddXV17Nu3z3UjrGmk+Pr16xk1ahQGg0HrEoW4Kq8JXVnpipY0jRTPzMxk3759MlJc+CyvCt2CggKtyxBeommkeNNDCnl5eUyYMIGpU6fy2GOPyUhx4bO8Kg7PmnAAAAibSURBVHS/+OILrcsQGmpoaPheExm73Y6iKKxevZqxY8fKGW7RIXhV6Mr2QudTVlZGdnY2WVlZ5OTkMGDAABRF4YUXXmDQoEGybSA6HAld4XH5+fmuhxSOHTtGXFyc60aYjBQXHZ1XhW5paanWZYh20NxIcUVRWLJkiYwUF52OV4WurHQ7jqqqKnJycsjMzCQ7O5sePXqgKApbtmxhyJAh8jSY6LS8JnSDg4NpbGyktraWwMBArcsR1+H8+fOup8EOHz7MqFGjMJlM3HffffTq1Uvr8oTwCl4Tulc2vZEuT77Bbrdz9OhR17bBxYsXmTx5MrfffjvPPfecNJERohleE7rw3RaDhK73ahop3vQ0mNFoxGQy8cgjjzBixAjZNhDiGrwydIV3+eabb8jKyiIrK4v9+/czdOhQkpKSWL58Of3799e6PCF8ioSu+BGHw8HJkydd2wZnzpwhISGB2267TUaKC3GDJHQF4Gwic+DAAdeNMIPB4JqkMGbMGPz8vOo/FSF8llf9SZLQ9aymkeIZGRns3buXmJgYFEWRkeJCtCOvC93Tp09rXUaH1dxI8fj4eBRF4ZFHHpGR4kJ4gNeF7qFDh7Quo0NpbGzk008/dQVtbW0tiqJw9913ExcXJ01khPAwrwtd2V64ceXl5a6nwfbs2UPfvn0xmUxs27aN2NhY2TYQQkMSuh1E00jxzMxMPv/8c8aOHYuiKKxdu1bOPQvhRbwqdGVkj/uaRoo3BW3TSPEFCxYQHx9PcHCw1iUKIZrhVaHbtNJ1OBzyI3AzqqqqyM3NdT2o0LVrV5KSknj88ccZNmyYPA0mhA/wqtANCgpCp9NRU1MjK7VvFRUVuVazn376KcOHD0dRFO655x769OmjdXlCiFbyqtCF71a7nTV0HQ4Hx44dczX5Pn/+PJMnT2bmzJls3bpVRooL4eO8NnR79+6tdSke0zRSPCMjg927dxMUFISiKDz88MMyUlyIDsYrQ7czTJD44Ujx2NhYFEXh97//PQMHDtS6PCFEO/HK0O2IJxgcDgd5eXmukeKnTp1i4sSJTJs2jccff5yIiAitSxRCeICEbjuqr6/n4MGDrhthDocDk8nEmjVrGDt2LP7+/lqXKITwMAndNtY0UjwzM5OPP/6YgQMHoigKL774Ij/5yU/kKJwQnZzXhW5ERAR5eXlal9Eq+fn5rtVs00jxpKQkfv7zn9OtWzetyxNCeBHvCl2Hg5719Vw4fhwOHICwMLjpJvCypiyNjY0cPnzYdayrsrISRVFYunQp8fHxMlhTCHFV2oduQwPs2gV/+Qvs3098RQWx5eXw8cfgcDhfj4mB+fPh9ttBo/aDlZWV3xsp3rNnTxRF4cknn5SR4kIIt+kcDsdVX4yLi3Ps37+//T59507YtAma9nBDQ6lpaOBcQQE/iYlx/p7dDjU1UFcHej0sXw7r14MHHp5oGimekZHBZ599xujRozGZTJhMJhkpLoS4Kp1Od8DhcMQ195o2K93KSvjlL8Fmg8BAuOK4lAHnj+8uej2EhDh/NTTAn/4EaWnw+utwyy1tWlbTSPGmY11NI8Xnzp3L888/LyPFhRA3zPOhW14OS5bAZ59BZKQzVK9gMBhobGzEAfzoPr+fn/M9hYUwbx789a8wduwNldPcSPGmSQoyUlwI0dY8G7p2O9x7Lxw5Al26QDPHp/Q6HTqdDrvdjuFqgWc0OlfLy5fD++9DdHSryrh48aKrU9eBAwe45ZZbUBRFRooLIdqdZ0P3v/8bcnOdq9UWzqs2rXavGroAoaFQWgoPPQT/+MePVsxX+uFI8bNnzzJx4kRSUlL49a9/LSPFhRAe47nQLSyErVudYXmNBwT8vg1drvXEltEIhw/D22/DggXfe6lppHhT0Pr5+ZGUlMTatWsZPXq0jBQXQmjCc8nz5pvOEwihodf8UoPBQGNDw7WvqdM5b8S9+irMn8/l0lLX02BNI8WTkpJ49dVXZaS4EMIreCZ0m04duHH3v6yxka1VVRwqL6dbYCAPRkWRcpVmMA6gTq+n9quveHX2bD4sK2PChAmYTCYZKS6E8EqeCd2vvnKetXWjAfe2Cxfw1+t5u3t3ikNCWHfmDLFBQcR8+5SXw+Ggqrqa8vJyKsrLcTgcRBkMLIqO5hcvvCAjxYUQXs0zoXv0qPPkwjVU2+2kl5fz+8hIAh0ORoeEkBQezvslJdwVHExFeTkVlZUE+PsTFh5Ov379CAwKQldRQWRRkdc9LiyEED/kmdDNy3Pu515Dfl0dBiA6MJDKykqKL12iS0UFn9TUMKdLF8LDw+nRsyf+P7wJFhDgXE0LIYSX80zoVlW1eKTL9WV2O6F6PcEhIRQXF6M3GOgZFkZAQAADWjo/q9e7FepCCKE1z4RuSIhb2wshej2VdjtBgYEMGjQIAHtxMWFXPhbcHLtdthaEED7BM8+4/uQnboXigIAAGnFuMzQ5UVvruol2VXV1zs8QQggv55nQHTr0mg9EAATr9UwLD+cPFy9SbbdzqKqKjPJyLNeaH1ZbCxMntlGxQgjRfjy30g0NdYbjNTzSqxe1djvJJ06w6dw5Nvbq1fJK1+FwNsJJTGzDgoUQon14Zk/XYIC774bf/tb5BFkLjAYDL7Sm6Ux1NfTuDXHNtq4UQgiv4rm+hfPnO/d12/KUgcPhvN6DD7p1OkIIIbTmuaTq0QMef9zZkrGFaRWtUloKo0fDHXe0zfWEEKKdeXZ5uGABJCQ4x/PcaPBWVDhH9rz4oqxyhRA+w7Nppdc7x+yMGuUMXjfO7jarrMx58+y//gsGDGjbGoUQoh15fokYGuoMy5kzndsDVVXuv7ehwRnWffrA//6vM7yFEMKHtDgNWKfTXQROe64cIYToEAY6HI6o5l5oMXSFEEK0LbkDJYQQHiShK4QQHiShK4QQHiShK4QQHiShK4QQHvT/AXZyCZ93hy7iAAAAAElFTkSuQmCC\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1051,19 +1133,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 24,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2019-08-22T01:58:30.581695Z",
-     "start_time": "2019-08-22T01:58:30.574381Z"
+     "end_time": "2019-12-12T18:21:15.433023Z",
+     "start_time": "2019-12-12T18:21:15.363917Z"
     }
    },
    "outputs": [
     {
      "data": {
       "text/html": [
-       "<h3>Version Information</h3><table><tr><th>Qiskit Software</th><th>Version</th></tr><tr><td>Qiskit</td><td>None</td></tr><tr><td>Terra</td><td>0.9.0</td></tr><tr><td>Aer</td><td>0.3.0</td></tr><tr><td>Ignis</td><td>0.2.0</td></tr><tr><td>Aqua</td><td>0.5.6</td></tr><tr><td>IBM Q Provider</td><td>0.3.2rc1</td></tr><tr><th>System information</th></tr><tr><td>Python</td><td>3.7.4 (default, Aug 13 2019, 15:17:50) \n",
-       "[Clang 4.0.1 (tags/RELEASE_401/final)]</td></tr><tr><td>OS</td><td>Darwin</td></tr><tr><td>CPUs</td><td>4</td></tr><tr><td>Memory (Gb)</td><td>16.0</td></tr><tr><td colspan='2'>Wed Aug 21 21:58:30 2019 EDT</td></tr></table>"
+       "<h3>Version Information</h3><table><tr><th>Qiskit Software</th><th>Version</th></tr><tr><td>Qiskit</td><td>0.14.0</td></tr><tr><td>Terra</td><td>0.11.0</td></tr><tr><td>Aer</td><td>0.3.4</td></tr><tr><td>Ignis</td><td>0.2.0</td></tr><tr><td>Aqua</td><td>0.6.1</td></tr><tr><td>IBM Q Provider</td><td>0.4.4</td></tr><tr><th>System information</th></tr><tr><td>Python</td><td>3.7.5 (default, Oct 25 2019, 10:52:18) \n",
+       "[Clang 4.0.1 (tags/RELEASE_401/final)]</td></tr><tr><td>OS</td><td>Darwin</td></tr><tr><td>CPUs</td><td>4</td></tr><tr><td>Memory (Gb)</td><td>16.0</td></tr><tr><td colspan='2'>Thu Dec 12 13:21:15 2019 EST</td></tr></table>"
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -1116,7 +1198,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.4"
+   "version": "3.7.5"
   },
   "varInspector": {
    "cols": {
diff --git a/qiskit/advanced/aqua/optimization/vehicle_routing.ipynb b/qiskit/advanced/aqua/optimization/vehicle_routing.ipynb
index 1e977d9e0..cbca7d75a 100644
--- a/qiskit/advanced/aqua/optimization/vehicle_routing.ipynb
+++ b/qiskit/advanced/aqua/optimization/vehicle_routing.ipynb
@@ -1,10 +1,15 @@
 {
  "cells": [
   {
+   "attachments": {
+    "qiskit_header.png": {
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"
+    }
+   },
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<img src=\"../../../../images/qiskit_header.png\" alt=\"Note: In order for images to show up in this jupyter notebook you need to select File => Trusted Notebook\" align=\"middle\">"
+    "![qiskit_header.png](attachment:qiskit_header.png)"
    ]
   },
   {
@@ -13,16 +18,7 @@
     "collapsed": true
    },
    "source": [
-    "# _*Qiskit Aqua: Vehicle Routing*_\n",
-    "\n",
-    "The latest version of this notebook is available on https://github.com/qiskit/qiskit-tutorials.\n",
-    "\n",
-    "***\n",
-    "### Contributors\n",
-    "Andrea Simonetto<sup>[1]</sup>, Jakub Marecek<sup>[1]</sup>, Martin Mevissen<sup>[1]</sup>\n",
-    "\n",
-    "### Affiliation\n",
-    "- <sup>[1]</sup>IBMQ\n",
+    "# Qiskit Aqua: Vehicle Routing\n",
     "\n",
     "## The Introduction\n",
     "\n",
@@ -192,8 +188,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
+   "execution_count": 6,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T18:04:31.743086Z",
+     "start_time": "2019-12-12T18:04:31.734552Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -241,8 +242,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
+   "execution_count": 7,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T18:04:32.943634Z",
+     "start_time": "2019-12-12T18:04:32.940809Z"
+    }
+   },
    "outputs": [],
    "source": [
     "# Initialize the problem by defining the parameters\n",
@@ -259,8 +265,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
+   "execution_count": 8,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T18:04:34.977606Z",
+     "start_time": "2019-12-12T18:04:34.970356Z"
+    }
+   },
    "outputs": [],
    "source": [
     "# Get the data\n",
@@ -290,8 +301,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
+   "execution_count": 9,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T18:04:35.248142Z",
+     "start_time": "2019-12-12T18:04:35.245334Z"
+    }
+   },
    "outputs": [],
    "source": [
     "# Initialize the problem by randomly generating the instance\n",
@@ -310,8 +326,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
+   "execution_count": 10,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T18:04:36.138868Z",
+     "start_time": "2019-12-12T18:04:36.114461Z"
+    }
+   },
    "outputs": [],
    "source": [
     "class ClassicalOptimizer:\n",
@@ -404,8 +425,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
+   "execution_count": 11,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T18:04:36.445275Z",
+     "start_time": "2019-12-12T18:04:36.441619Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -425,8 +451,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
+   "execution_count": 12,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T18:04:36.764240Z",
+     "start_time": "2019-12-12T18:04:36.758126Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -452,8 +483,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
+   "execution_count": 13,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T18:04:37.130306Z",
+     "start_time": "2019-12-12T18:04:37.119315Z"
+    }
+   },
    "outputs": [],
    "source": [
     "# Visualize the solution\n",
@@ -504,8 +540,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
+   "execution_count": 14,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T18:04:38.212743Z",
+     "start_time": "2019-12-12T18:04:38.166765Z"
+    }
+   },
    "outputs": [],
    "source": [
     "class QuantumOptimizer:\n",
@@ -718,8 +759,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
+   "execution_count": 15,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T18:04:39.155393Z",
+     "start_time": "2019-12-12T18:04:39.151429Z"
+    }
+   },
    "outputs": [],
    "source": [
     "# Instantiate the quantum optimizer class with parameters: \n",
@@ -739,8 +785,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
+   "execution_count": 16,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T18:04:40.049014Z",
+     "start_time": "2019-12-12T18:04:40.041132Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -780,9 +831,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
+   "execution_count": 17,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T18:04:49.559905Z",
+     "start_time": "2019-12-12T18:04:41.834062Z"
+    }
+   },
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/paul/opt/anaconda3/envs/qiskit/lib/python3.7/site-packages/qiskit/providers/models/backendconfiguration.py:337: UserWarning: `dt` and `dtm` now have units of seconds(s) rather than nanoseconds(ns).\n",
+      "  warnings.warn('`dt` and `dtm` now have units of seconds(s) rather '\n"
+     ]
+    },
     {
      "name": "stdout",
      "output_type": "stream",
@@ -812,8 +876,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
+   "execution_count": 18,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T18:05:28.961789Z",
+     "start_time": "2019-12-12T18:04:49.570942Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -839,14 +908,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 19,
    "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T18:05:29.319300Z",
+     "start_time": "2019-12-12T18:05:28.986239Z"
+    },
     "scrolled": true
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -887,20 +960,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
+   "execution_count": 20,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-12-12T18:05:49.104581Z",
+     "start_time": "2019-12-12T18:05:29.337515Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[1 1 1 0 1 0]\n",
-      "132.11148115684045\n"
+      "[1 1 1 0 0 1]\n",
+      "12434.909288240102\n"
      ]
     }
    ],
    "source": [
-    "from qiskit import BasicAer\n",
+    "from qiskit import Aer\n",
     "from qiskit.aqua import QuantumInstance\n",
     "from qiskit.aqua import run_algorithm\n",
     "from qiskit.aqua.input import EnergyInput\n",
@@ -910,7 +988,7 @@
     "from qiskit.aqua.translators.ising.vehicle_routing import *\n",
     "\n",
     "qubitOp = get_vehiclerouting_qubitops(instance, n, K)\n",
-    "backend = BasicAer.get_backend('statevector_simulator')\n",
+    "backend = Aer.get_backend('statevector_simulator')\n",
     "seed = 50\n",
     "cobyla = COBYLA()\n",
     "cobyla.set_options(maxiter=250)\n",
@@ -928,19 +1006,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 21,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2019-08-22T01:59:00.675931Z",
-     "start_time": "2019-08-22T01:59:00.666845Z"
+     "end_time": "2019-12-12T18:05:49.205400Z",
+     "start_time": "2019-12-12T18:05:49.123796Z"
     }
    },
    "outputs": [
     {
      "data": {
       "text/html": [
-       "<h3>Version Information</h3><table><tr><th>Qiskit Software</th><th>Version</th></tr><tr><td>Qiskit</td><td>None</td></tr><tr><td>Terra</td><td>0.9.0</td></tr><tr><td>Aer</td><td>0.3.0</td></tr><tr><td>Ignis</td><td>0.2.0</td></tr><tr><td>Aqua</td><td>0.5.6</td></tr><tr><td>IBM Q Provider</td><td>0.3.2rc1</td></tr><tr><th>System information</th></tr><tr><td>Python</td><td>3.7.4 (default, Aug 13 2019, 15:17:50) \n",
-       "[Clang 4.0.1 (tags/RELEASE_401/final)]</td></tr><tr><td>OS</td><td>Darwin</td></tr><tr><td>CPUs</td><td>4</td></tr><tr><td>Memory (Gb)</td><td>16.0</td></tr><tr><td colspan='2'>Wed Aug 21 21:59:00 2019 EDT</td></tr></table>"
+       "<h3>Version Information</h3><table><tr><th>Qiskit Software</th><th>Version</th></tr><tr><td>Qiskit</td><td>0.14.0</td></tr><tr><td>Terra</td><td>0.11.0</td></tr><tr><td>Aer</td><td>0.3.4</td></tr><tr><td>Ignis</td><td>0.2.0</td></tr><tr><td>Aqua</td><td>0.6.1</td></tr><tr><td>IBM Q Provider</td><td>0.4.4</td></tr><tr><th>System information</th></tr><tr><td>Python</td><td>3.7.5 (default, Oct 25 2019, 10:52:18) \n",
+       "[Clang 4.0.1 (tags/RELEASE_401/final)]</td></tr><tr><td>OS</td><td>Darwin</td></tr><tr><td>CPUs</td><td>4</td></tr><tr><td>Memory (Gb)</td><td>16.0</td></tr><tr><td colspan='2'>Thu Dec 12 13:05:49 2019 EST</td></tr></table>"
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -992,7 +1070,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.4"
+   "version": "3.7.5"
   },
   "varInspector": {
    "cols": {