Skip to main content

[MPNUM]

A matrix product representation library for Python

mpnum is a flexible, user-friendly, and expandable toolbox for the matrix product state/tensor train tensor format. mpnum provides:

  • support for well-known matrix product representations, such as:

  • matrix product states (MPS), also known as tensor trains (TT)

  • matrix product operators (MPO)

  • local purification matrix product states (PMPS)

  • arbitrary matrix product arrays (MPA)

  • arithmetic operations: addition, multiplication, contraction etc.

  • compression, canonical forms, etc.

  • finding extremal eigenvalues and eigenvectors of MPOs (DMRG)

  • flexible tools for new matrix product algorithms

To install the latest stable version run

pip install mpnum

If you want to install mpnum from source, please run (on Unix)

git clone https://github.com/dseuss/mpnum.git cd mpnum pip install .

In order to run the tests and build the documentation, you have to install the development dependencies via

pip install -r requirements.txt

For more information, see:

  • Introduction to mpnum

  • Notebook with code examples

  • Library reference

  • Contribution Guidelines

Required packages:

  • six, numpy, scipy

Supported Python versions:

  • 2.7, 3.4, 3.5, 3.6

Alternatives:

  • TT-Toolbox for Matlab

  • ttpy for Python

  • ITensor for C++

How to contribute

Contributions of any kind are very welcome. Please use the issue tracker for bug reports. If you want to contribute code, please see the section on how to contribute in the documentation.

Contributors

License

Distributed under the terms of the BSD 3-Clause License (see LICENSE).

Citations

mpnum has been used and cited in the following publications:

    1. Dhand et al. (2017), arXiv 1710.06103

    1. Schwartz, J. Scheuer et al. (2017), arXiv 1710.01508

    1. Scheuer et al. (2017), arXiv 1706.01315

      1. Lanyon, Ch. Maier et al, Nat. Phys. (2017), arXiv 1612.08000

Metadata

Release files for mpnum 1.0.2

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for mpnum 1.0.2
File Size Uploaded
mpnum-1.0.2.tar.gz 116.6 kB Details

Built distribution (wheel)

Table of built distributions (wheels) for mpnum 1.0.2
File Interpreter ABI Platform
mpnum-1.0.2-py2.py3-none-any.whl Python 2, Python 3 none any Details

Total release size: 195.0 kB

Release files / mpnum-1.0.2.tar.gz

Download URL mpnum-1.0.2.tar.gz
Size 116.6 kB
Tags Source
SHA-256 checksum
How to use checksums
2f19abf9ad196a5b45c4a6244dfddaf4edad3e7d56e650cb09f7a648dda61841
BLAKE2b-256 checksum
How to use checksums
86643f7014f9c3fcd54aa37d3d5db7f9a5dcdc69574ab20fc5f49c265175e69b
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No

Release files / mpnum-1.0.2-py2.py3-none-any.whl

Download URL mpnum-1.0.2-py2.py3-none-any.whl
Size 78.4 kB
Tags Python 2 Python 3
SHA-256 checksum
How to use checksums
643d6fa65c7541e3cb5b92b101ae7758c0212d55ac0127d5533f0e0b73eb503d
BLAKE2b-256 checksum
How to use checksums
a0c6849372e4881b6d8ee18fd0a6e678ce08e9a5a67b3d6e182d5cec290ba047
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No

Release history Release notifications | RSS feed

This release

1.0.2 This release

2 release files

1.0.1

2 release files

1.0.0

2 release files

0.2.2

1 release file

0.2.1

1 release file

0.2

0.1.2

1 release file

0.1.1

1 release file

0.1

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page