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        "author": "Johnnie Gray",
        "author_email": "john.gray.14@ucl.ac.uk",
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            "Programming Language :: Python :: 3",
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        "description": ".. image:: https://travis-ci.org/jcmgray/quimb.svg?branch=master\n  :target: https://travis-ci.org/jcmgray/quimb\n  :alt: Travis-CI\n.. image:: https://codecov.io/gh/jcmgray/quimb/branch/master/graph/badge.svg\n  :target: https://codecov.io/gh/jcmgray/quimb\n  :alt: Code Coverage\n.. image:: https://img.shields.io/lgtm/grade/python/g/jcmgray/quimb.svg\n  :target: https://lgtm.com/projects/g/jcmgray/quimb/\n  :alt: Code Quality\n.. image:: https://readthedocs.org/projects/quimb/badge/?version=latest\n  :target: http://quimb.readthedocs.io/en/latest/?badge=latest\n  :alt: Documentation Status\n.. image:: http://joss.theoj.org/papers/10.21105/joss.00819/status.svg\n  :target: https://doi.org/10.21105/joss.00819\n  :alt: JOSS Paper\n.. image:: https://badges.gitter.im/quimb-chat/community.svg\n  :target: https://gitter.im/quimb-chat/community?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge\n  :alt: Gitter\n\n\n----------------------------------------------------------------------------------\n\n`quimb <https://github.com/jcmgray/quimb>`_ is an easy but fast python library for quantum information and many-body calculations, including with tensor networks. The code is hosted on `github <https://github.com/jcmgray/quimb>`_, do please submit any issues or pull requests there. It is also thoroughly unit-tested and the tests might be the best place to look for detailed documentation.\n\nThe **core** ``quimb`` module:\n\n* Uses straight ``numpy`` and ``scipy.sparse`` matrices as quantum objects\n* Accelerates and parallelizes many operations using `numba <https://numba.pydata.org>`_.\n* Makes it easy to construct operators in large tensor spaces (e.g. 2D lattices)\n* Uses efficient methods to compute various quantities including entanglement measures\n* Has many built-in states and operators, including those based on fast, parallel random number generation\n* Can perform evolutions with several methods, computing quantities on the fly\n* Has an optional `slepc4py <https://bitbucket.org/slepc/slepc4py>`_ interface for easy distributed (MPI) linear algebra. This can massively increase the performance when seeking, for example, mid-spectrum eigenstates\n\nThe **tensor network** submodule ``quimb.tensor``:\n\n* Uses a geometry free representation of tensor networks\n* Uses `opt_einsum <https://github.com/dgasmith/opt_einsum>`_ to find efficient contraction orders for hundreds or thousands of tensors\n* Can perform those contractions on various backends, including with a GPU\n* Can plot any network, color-coded, with bond size represented\n* Can treat any network as a scipy ``LinearOperator``, allowing many decompositions\n* Can perform DMRG1, DMRG2 and DMRGX, in matrix product state language\n* Has tools to efficiently address periodic problems (transfer matrix compression and pseudo-orthogonalization)\n* Can perform MPS time evolutions with TEBD\n* Can optimize arbitrary tensor networks with ``tensorflow`` or ``pytorch``\n\nThe **full documentation** can be found at: `<http://quimb.readthedocs.io/en/latest/>`_.\nContributions of any sort are very welcome - please see the `contributing guide <https://github.com/jcmgray/quimb/blob/develop/.github/CONTRIBUTING.md>`_. For 'non-github-issue' questions there is a `gitter chat <https://gitter.im/quimb-chat/>`_.\n\n\n",
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