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            "Natural Language :: English",
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        "description": "The Walrus\n##########\n\n.. image:: https://circleci.com/gh/XanaduAI/thewalrus/tree/master.svg?style=svg&circle-token=209b57390082a2b2fe2cdc9ee49a301ddc29ca5b\n    :alt: CircleCI\n    :target: https://circleci.com/gh/XanaduAI/thewalrus/tree/master\n\n.. image:: https://ci.appveyor.com/api/projects/status/9udscqldo1xd25yk/branch/master?svg=true\n    :alt: Appveyor\n    :target: https://ci.appveyor.com/project/josh146/hafnian/branch/master\n\n.. image:: https://img.shields.io/codecov/c/github/xanaduai/thewalrus/master.svg?style=flat\n    :alt: Codecov coverage\n    :target: https://codecov.io/gh/XanaduAI/thewalrus\n\n.. image:: https://img.shields.io/codacy/grade/df94d22534cf4c05b1bddcf697011a82.svg?style=flat\n    :alt: Codacy grade\n    :target: https://app.codacy.com/app/XanaduAI/thewalrus?utm_source=github.com&utm_medium=referral&utm_content=XanaduAI/thewalrus&utm_campaign=badger\n\n.. image:: https://img.shields.io/readthedocs/the-walrus.svg?style=flat\n    :alt: Read the Docs\n    :target: https://the-walrus.readthedocs.io\n\n.. image:: https://img.shields.io/pypi/pyversions/thewalrus.svg?style=flat\n    :alt: PyPI - Python Version\n    :target: https://pypi.org/project/thewalrus\n\nA library for the calculation of hafnians, Hermite polynomials and Gaussian boson sampling. For more information, please see the `documentation <https://the-walrus.readthedocs.io>`_.\n\nFeatures\n========\n\n* Fast calculation of hafnians, loop hafnians, and torontonians of general and certain structured matrices.\n\n* An easy to use interface to use the loop hafnian for Gaussian quantum state calculations.\n\n* Sampling algorithms for hafnian and torontonians of graphs.\n\n* Efficient classical methods for approximating the hafnian of non-negative matrices.\n\n* Easy to use implementations of the multidimensional Hermite polynomials, which can also be used to calculate hafnians of all reductions of a given matrix.\n\n\nInstallation\n============\n\nPre-built binary wheels are available for the following platforms:\n\n+------------+-------------+------------------+---------------+\n|            | macOS 10.6+ | manylinux x86_64 | Windows 64bit |\n+============+=============+==================+===============+\n| Python 3.5 |  \u2705         |  \u2705              |   \u2705          |\n+------------+-------------+------------------+---------------+\n| Python 3.6 |  \u2705         |  \u2705              |   \u2705          |\n+------------+-------------+------------------+---------------+\n| Python 3.7 |  \u2705         |  \u2705              |   \u2705          |\n+------------+-------------+------------------+---------------+\n\nTo install, simply run\n\n.. code-block:: bash\n\n    pip install thewalrus\n\n\nCompiling from source\n=====================\n\nThe Walrus depends on the following Python packages:\n\n* `Python <http://python.org/>`_ >=3.5\n* `NumPy <http://numpy.org/>`_  >=1.13.3\n\nIn addition, to compile the C++ extension, the following dependencies are required:\n\n* A C++11 compiler, such as ``g++`` >= 4.8.1, ``clang`` >= 3.3, ``MSVC`` >= 14.0/2015\n* `Eigen3 <http://eigen.tuxfamily.org/index.php?title=Main_Page>`_ - a C++ header library for linear algebra.\n\nOn Debian-based systems, these can be installed via ``apt`` and ``curl``:\n\n.. code-block:: console\n\n    $ sudo apt install g++ libeigen3-dev\n\nor using Homebrew on MacOS:\n\n.. code-block:: console\n\n    $ brew install gcc eigen\n\nAlternatively, you can download the Eigen headers manually:\n\n.. code-block:: console\n\n    $ mkdir ~/.local/eigen3 && cd ~/.local/eigen3\n    $ wget http://bitbucket.org/eigen/eigen/get/3.3.7.tar.gz -O eigen3.tar.gz\n    $ tar xzf eigen3.tar.gz eigen-eigen-323c052e1731/Eigen --strip-components 1\n    $ export EIGEN_INCLUDE_DIR=$HOME/.local/eigen3\n\nNote that we export the environment variable ``EIGEN_INCLUDE_DIR`` so that The Walrus can find the Eigen3 header files (if not provided, The Walrus will by default look in ``/use/include/eigen3`` and ``/usr/local/include/eigen3``).\n\nOnce all dependencies are installed, you can compile the latest stable version of the The Walrus library as follows:\n\n.. code-block:: console\n\n    $ python -m pip install thewalrus --no-binary :all:\n\nAlternatively, you can compile the latest development version by cloning the git repository, and installing using pip in development mode.\n\n.. code-block:: console\n\n    $ git clone https://github.com/XanaduAI/thewalrus.git\n    $ cd thewalrus && python -m pip install -e .\n\n\nOpenMP\n------\n\n``libwalrus`` uses OpenMP to parallelize both the permanent and the hafnian calculation. **At the moment, this is only supported on Linux using the GNU g++ compiler, due to insufficient support using Windows/MSCV and MacOS/Clang.**\n\n\n\nUsing LAPACK, OpenBLAS, or MKL\n------------------------------\n\nIf you would like to take advantage of the highly optimized matrix routines of LAPACK, OpenBLAS, or MKL, you can optionally compile the ``libwalrus`` such that Eigen uses these frameworks as backends. As a result, all calls in the ``libwalrus`` library to Eigen functions are silently substituted with calls to LAPACK/OpenBLAS/MKL.\n\nFor example, for LAPACK integration, make sure you have the ``lapacke`` C++ LAPACK bindings installed (``sudo apt install liblapacke-dev`` in Ubuntu-based Linux distributions), and then compile with the environment variable ``USE_LAPACK=1``:\n\n.. code-block:: console\n\n    $ USE_LAPACK=1 python -m pip install thewalrus --no-binary :all:\n\nAlternatively, you may pass ``USE_OPENBLAS=1`` to use the OpenBLAS library.\n\n\nSoftware tests\n==============\n\nTo ensure that The Walrus library is working correctly after installation, the test suite can be run by navigating to the source code folder and running\n\n.. code-block:: console\n\n    $ make test\n\nTo run the low-level C++ test suite, `Googletest <https://github.com/google/googletest>`_\nwill need to be installed. 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All contributors to The Walrus will be listed as authors on the releases.\n\nWe also encourage bug reports, suggestions for new features and enhancements, and even links to projects, applications or scientific publications that use The Walrus.\n\nAuthors\n=======\n\nNicol\u00e1s Quesada, Brajesh Gupt, and Josh Izaac.\n\nAll contributions are acknowledged in the `acknowledgments page <https://github.com/XanaduAI/thewalrus/blob/master/.github/ACKNOWLEDGMENTS.md>`_.\n\nIf you are doing research using The Walrus, please cite `our paper <https://dl.acm.org/citation.cfm?id=3325111>`_:\n\n Andreas Bj\u00f6rklund, Brajesh Gupt, and Nicol\u00e1s Quesada. 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