Metadata-Version: 2.1
Name: quimb
Version: 0.4.4
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: MIT
Keywords: quantum physics tensor networks tensors dmrg tebd
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Requires-Python: >=3.5
Provides-Extra: tensor
Provides-Extra: docs
Provides-Extra: random
Provides-Extra: advanced_solvers
Provides-Extra: tests
Requires-Dist: numpy (>=1.12)
Requires-Dist: scipy (>=1.0.0)
Requires-Dist: numba (>=0.39)
Requires-Dist: numexpr (>=2.3)
Requires-Dist: psutil (>=4.3.1)
Requires-Dist: cytoolz (>=0.8.0)
Requires-Dist: tqdm (>=4)
Requires-Dist: opt-einsum (>=2)
Provides-Extra: advanced_solvers
Requires-Dist: mpi4py; extra == 'advanced_solvers'
Requires-Dist: petsc4py; extra == 'advanced_solvers'
Requires-Dist: slepc4py; extra == 'advanced_solvers'
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Provides-Extra: tensor
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Requires-Dist: networkx; extra == 'tensor'
Provides-Extra: tests
Requires-Dist: coverage; extra == 'tests'
Requires-Dist: pytest; extra == 'tests'
Requires-Dist: pytest-cov; extra == 'tests'

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   :alt: Documentation Status

----------------------------------------------------------------------------------

`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 a wide variety of built-in states and operators, including those based on fast, multi-threaded 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 of tensors, and perform those contractions potentially on the 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>`_.


