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EntropicaQAOA is a modular package for the quantum approximate optimisation algorithm (QAOA) built on top of Rigetti’s Forest SDK.

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Entropica QAOA

A modular package providing multiple features and workflow tools for the quantum approximate optimisation algorithm (QAOA), facilitating its use, prototyping, and testing. Includes several different parametrisations, integration with data science and graph analysis libraries such as Pandas and NetworkX, numerous utility functions, and convenient optimiser logging and analysis tools. Documentation contains extensive and didactic examples.

Read more about EntropicaQAOA on our blog.

Documentation

The documentation for EntropicaQAOA can be found here. Alternatively, it can be complied locally as follows:

Install the Prerequisites

pip install sphinx sphinx-rtd-theme sphinx-autodoc-typehints nbsphinx nbconvert

Compile the documentation

cd docs && make html

The compiled HTML version of the documentation is then found in entropica_qaoa/docs/build/html.

Installation

We assume that the user has already installed Rigetti's pyQuil package, as well as the Rigetti QVM and Quil Compiler. For instructions on how to do so, see Rigetti's documentation here.

You can install the EntropicaQAOA package using pip:

pip install entropica-qaoa

To upgrade to the latest version:

pip install --upgrade entropica-qaoa

If you want to run the Demo Notebooks, you will additionally need to install scikit-learn and scikit-optimize, which can be done as follows:

pip install scikit-learn && pip install scikit-optimize

Alternatively, you can clone directly from GitHub:

git clone https://github.com/entropicalabs/entropica_qaoa.git

Testing

All software tests are located in entropica_qaoa/tests/. To run them you will need to install pytest. To speed up the testing, we have tagged tests that require more computational time (~ 5 mins or so) with runslow, and the tests of the notebooks with notebooks. The commands are as follows:

  • pytest runs the default tests, and skips both the longer tests that need heavier simulations, as well as tests of the Notebooks in the examples directory.
  • pytest --runslow runs the the tests that require longer time.
  • pytest --notebooks runs the Notebook tests. To achieve this, the notebooks are converted to python scripts, and then executed. Should any errors occur, this means that the line numbers given in the error messages refer to the lines in <TheNotebook>.py, and not in <TheNotebook>.ipynb.
  • pytest --all runs all of the above tests.

QPU access

EntropicaQAOA provides full native support for Rigetti’s QVM and QPUs. For access to the QPUs, sign up online at https://qcs.rigetti.com/, or reach out to [email protected].

Contributing and feedback

If you find any bugs or errors, have feature requests, or code you would like to contribute, feel free to open an issue or send us a pull request on GitHub .

We are always interested to hear about projects built with EntropicaQAOA. If you have an application you’d like to tell us about, drop us an email at [email protected].

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EntropicaQAOA is a modular package for the quantum approximate optimisation algorithm (QAOA) built on top of Rigetti’s Forest SDK.

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