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:obj:`~qgym` – A Gym for Training and Benchmarking RL-Based Quantum Compilation
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:obj:`~qgym` is a software framework that provides environments for training and benchmarking RL-based quantum compilers.
It is built on top of OpenAI Gym and abstracts parts of the compilation process that are irrelevant to AI researchers.
:obj:`~qgym` includes three environments: :class:`~qgym.envs.InitialMapping`,
:class:`~qgym.envs.Routing`, and :class:`~qgym.envs.Scheduling`,
each of which is customizable and extensible.


Documentation
==============
We have created an `extensive documentation <https://qutech-delft.github.io/qgym/index.html>`_ with code snippets.
Please feel free to contact us via `s.feld@tudelft.nl <s.feld@tudelft.nl>`_ if you have any questions, or by creating a `GitHub issue <https://github.com/QuTech-Delft/qgym/issues/new>`_.


Getting Started
===============
What follows are some simple steps to get you running.
You could also have a look at some `Jupyter Notebooks <https://github.com/QuTech-Delft/qgym/tree/master/notebooks>`_ that we have created for a tutorial at the `IEEE International Conference on Quantum Computing and Engineering (QCE’22) <https://qce.quantum.ieee.org/2022/tutorials-program/>`_.

Installing with pip
-------------------
To install the :obj:`~qgym` use

.. code-block:: console

   pip install qgym

If you would also like to use the notebooks, additional packages are required, which can
simply be installed by using:

.. code-block:: console

   pip install qgym[tutorial]

Currently :obj:`~qgym` has support for Python 3.7, 3.8, 3.9, 3.10 and 3.11.


Publication
============
The paper on :obj:`~qgym` has been presented in the `1st International Workshop on Quantum Machine Learning: From Foundations to Applications (QML@QCE'23) <https://qml.lfdr.de/2023/>`_.
The publication can be found on `computer.org <https://www.computer.org/csdl/proceedings-article/qce/2023/432302a026/1SuQRWR5uCI>`_.
You can find the preprint of the paper on `arxiv <https://arxiv.org/pdf/2308.02536.pdf>`_.

.. code-block:: console

   @inproceedings{van2023qgym,
   title={qgym: A Gym for training and benchmarking RL-based quantum compilation},
   author={Van Der Linde, Stan and De Kok, Willem and Bontekoe, Tariq and Feld, Sebastian},
   booktitle={2023 IEEE International Conference on Quantum Computing and Engineering (QCE)},
   volume={2},
   pages={26--30},
   year={2023},
   organization={IEEE}
   }

Team
====
Building :obj:`~qgym` is a joint effort.

Core developers
----------------
- `Stan van der Linde <https://scholar.google.com/citations?user=MyQ0mBcAAAAJ&hl=nl>`_
- `Willem de Kok <https://nl.linkedin.com/in/willem-de-kok-a74942187>`_
- `Tariq Bontekoe <https://nl.linkedin.com/in/tariq-bontekoe-53214817a>`_
- `Sebastian Feld <https://nl.linkedin.com/in/sebastian-feld?>`_

Contributors and Power Users
-----------------------------
- `Joris Henstra <https://www.linkedin.com/in/jorishenstra/>`_
- `Rares Oancea <https://www.linkedin.com/in/rares-adrian-oancea-8a67b0204/>`_ 

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   :maxdepth: 4
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   qgym