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        "description": "[MPNUM]\n\n\nA matrix product representation library for Python\n\nmpnum is a flexible, user-friendly, and expandable toolbox for the\nmatrix product state/tensor train tensor format. mpnum provides:\n\n-   support for well-known matrix product representations, such as:\n-   matrix product states (MPS), also known as tensor trains (TT)\n-   matrix product operators (MPO)\n-   local purification matrix product states (PMPS)\n-   arbitrary matrix product arrays (MPA)\n-   arithmetic operations: addition, multiplication, contraction etc.\n-   compression, canonical forms, etc.\n-   finding extremal eigenvalues and eigenvectors of MPOs (DMRG)\n-   flexible tools for new matrix product algorithms\n\nTo install the latest stable version run\n\n    pip install mpnum\n\nIf you want to install mpnum from source, please run (on Unix)\n\n    git clone https://github.com/dseuss/mpnum.git\n    cd mpnum\n    pip install .\n\nIn order to run the tests and build the documentation, you have to\ninstall the development dependencies via\n\n    pip install -r requirements.txt\n\nFor more information, see:\n\n-   Introduction to mpnum\n-   Notebook with code examples\n-   Library reference\n-   Contribution Guidelines\n\nRequired packages:\n\n-   six, numpy, scipy\n\nSupported Python versions:\n\n-   2.7, 3.4, 3.5, 3.6\n\nAlternatives:\n\n-   TT-Toolbox for Matlab\n-   ttpy for Python\n-   ITensor for C++\n\n\nHow to contribute\n\nContributions of any kind are very welcome. Please use the issue tracker\nfor bug reports. If you want to contribute code, please see the section\non how to contribute in the documentation.\n\n\nContributors\n\n-   Daniel Suess, daniel@dsuess.me, University of Cologne\n-   Milan Holzaepfel, mail@mholzaepfel.de, Ulm University\n\n\nLicense\n\nDistributed under the terms of the BSD 3-Clause License (see LICENSE).\n\n\nCitations\n\nmpnum has been used and cited in the following publications:\n\n-   I. Dhand et al. (2017), arXiv 1710.06103\n-   I. Schwartz, J. Scheuer et al. (2017), arXiv 1710.01508\n-   J. Scheuer et al. (2017), arXiv 1706.01315\n-   B. P. Lanyon, Ch. Maier et al, Nat. Phys. (2017), arXiv 1612.08000\n\n\n\n",
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