Metadata-Version: 2.1
Name: quimb
Version: 1.1.2
Summary: Quantum information and many-body library.
Home-page: http://quimb.readthedocs.io
Author: Johnnie Gray
Author-email: john.gray.14@ucl.ac.uk
License: Apache
Description: .. image:: https://travis-ci.org/jcmgray/quimb.svg?branch=master
          :target: https://travis-ci.org/jcmgray/quimb
          :alt: Travis-CI
        .. image:: https://codecov.io/gh/jcmgray/quimb/branch/master/graph/badge.svg
          :target: https://codecov.io/gh/jcmgray/quimb
          :alt: Code Coverage
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          :target: https://lgtm.com/projects/g/jcmgray/quimb/
          :alt: Code Quality
        .. image:: https://readthedocs.org/projects/quimb/badge/?version=latest
          :target: http://quimb.readthedocs.io/en/latest/?badge=latest
          :alt: Documentation Status
        .. image:: http://joss.theoj.org/papers/10.21105/joss.00819/status.svg
          :target: https://doi.org/10.21105/joss.00819
          :alt: JOSS Paper
        
        
        ----------------------------------------------------------------------------------
        
        `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.
        
        The **core** ``quimb`` module:
        
        * Uses straight ``numpy`` and ``scipy.sparse`` matrices as quantum objects
        * Accelerates many operations using `numba <https://numba.pydata.org>`_ and `numexpr <https://github.com/pydata/numexpr>`_
        * Makes it easy to construct operators in large tensor spaces (e.g. 2D lattices)
        * Uses efficient methods to compute various quantities including entanglement measures
        * Has many built-in states and operators, including those based on fast, parallel random number generation
        * Can perform evolutions with several methods, computing quantities on the fly
        * 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
        
        The **tensor network** submodule ``quimb.tensor``:
        
        * Uses a geometry free representation of tensor networks
        * Uses `opt_einsum <https://github.com/dgasmith/opt_einsum>`_ to find efficient contraction orders for hundreds or thousands of tensors
        * Can perform those contractions on various backends, including with a GPU
        * Can plot any network, color-coded, with bond size represented
        * Can treat any network as a scipy ``LinearOperator``, allowing many decompositions
        * Can perform DMRG1, DMRG2 and DMRGX, in matrix product state language
        * Has tools to efficiently address periodic problems (transfer matrix compression and pseudo-orthogonalization)
        * Can perform MPS time evolutions with TEBD
        
        The **full documentation** can be found at: `<http://quimb.readthedocs.io/en/latest/>`_.
        Contributions of any sort are very welcome - please see the `contributing guide <https://github.com/jcmgray/quimb/blob/develop/.github/CONTRIBUTING.md>`_.
        
Keywords: quantum physics tensor networks tensors dmrg tebd
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Requires-Python: >=3.5
Provides-Extra: random
Provides-Extra: tensor
Provides-Extra: advanced_solvers
Provides-Extra: tests
Provides-Extra: docs
