quimb

quimb: A python package for quantum information and many-body calculations - Published in JOSS (2018)

https://github.com/jcmgray/quimb

Science Score: 95.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • CITATION.cff file
  • codemeta.json file
    Found codemeta.json file
  • .zenodo.json file
    Found .zenodo.json file
  • DOI references
    Found 4 DOI reference(s) in README and JOSS metadata
  • Academic publication links
    Links to: joss.theoj.org
  • Committers with academic emails
    4 of 23 committers (17.4%) from academic institutions
  • Institutional organization owner
  • JOSS paper metadata
    Published in Journal of Open Source Software

Keywords

dmrg entanglement mera peps physics python python3 quantum quantum-circuit quantum-circuit-simulator quantum-computing tebd tensor-network tensor-networks tensors

Scientific Fields

Mathematics Computer Science - 34% confidence
Last synced: 4 months ago · JSON representation

Repository

A python library for quantum information and many-body calculations including tensor networks.

Basic Info
Statistics
  • Stars: 586
  • Watchers: 15
  • Forks: 126
  • Open Issues: 73
  • Releases: 22
Topics
dmrg entanglement mera peps physics python python3 quantum quantum-circuit quantum-circuit-simulator quantum-computing tebd tensor-network tensor-networks tensors
Created about 10 years ago · Last pushed 4 months ago
Metadata Files
Readme Contributing License

README.md

quimb logo

Tests Code Coverage Code Quality Documentation Status JOSS Paper PyPI Anaconda-Server Badge

quimb is an easy but fast python library for 'quantum information many-body' calculations, focusing primarily on tensor networks. The code is hosted on github, and docs are hosted on readthedocs. Functionality is split in two:


The quimb.tensor module contains tools for working with tensors and tensor networks. It has a particular focus on automatically handling arbitrary geometry, e.g. beyond 1D and 2D lattices. With this you can:

  • construct and manipulate arbitrary (hyper) graphs of tensor networks
  • automatically contract, optimize and draw networks
  • use various backend array libraries such as jax and torch via autoray, including symmetries and fermions via symmray
  • run specific MPS, PEPS, MERA and quantum circuit algorithms, such as DMRG & TEBD

tensor pic


The core quimb module contains tools for reference 'exact' quantum calculations, where the states and operator are represented as either numpy.ndarray or scipy.sparse matrices. With this you can:

  • construct operators in complicated tensor spaces
  • find groundstates, excited states and do time evolutions, including with slepc
  • compute various quantities including entanglement measures
  • take advantage of numba accelerations
  • stochastically estimate $\mathrm{Tr}f(X)$ quantities

matrix pic


The full documentation can be found at: quimb.readthedocs.io. Contributions of any sort are very welcome - please see the contributing guide. Issues and pull requests are hosted on github. For other questions and suggestions, please use the discussions page.

Owner

  • Name: Johnnie Gray
  • Login: jcmgray
  • Kind: user
  • Company: Caltech

JOSS Publication

quimb: A python package for quantum information and many-body calculations
Published
September 04, 2018
Volume 3, Issue 29, Page 819
Authors
Johnnie Gray ORCID
University College London, London, UK
Editor
Jed Brown ORCID
Tags
python quantum physics tensor networks DMRG TEBD

GitHub Events

Total
  • Create event: 10
  • Release event: 5
  • Issues event: 57
  • Watch event: 87
  • Delete event: 6
  • Issue comment event: 91
  • Push event: 131
  • Pull request event: 13
  • Fork event: 17
Last Year
  • Create event: 10
  • Release event: 5
  • Issues event: 58
  • Watch event: 87
  • Delete event: 6
  • Issue comment event: 92
  • Push event: 133
  • Pull request event: 13
  • Fork event: 17

Committers

Last synced: 5 months ago

All Time
  • Total Commits: 2,101
  • Total Committers: 23
  • Avg Commits per committer: 91.348
  • Development Distribution Score (DDS): 0.03
Past Year
  • Commits: 208
  • Committers: 3
  • Avg Commits per committer: 69.333
  • Development Distribution Score (DDS): 0.048
Top Committers
Name Email Commits
Johnnie Gray j****y@g****m 2,037
Sergio Sánchez Ramírez s****z@b****s 13
Kevin J. Sung k****g@u****u 12
Adam Callison c****m@g****m 12
erika e****e@c****u 5
Aidan Dang d****g@a****g 2
Lee J. O'Riordan l****e@x****i 2
lm1909 l****9@g****m 2
vonDonnerstein d****r@g****m 2
ChienKaiMa k****y@g****m 1
Chris Self c****0@g****m 1
Drew d****r 1
Fabian Köhler f****r@p****h 1
Henry Makhanov m****v@u****u 1
Jaron Maene J****e@g****m 1
Julien Drapeau 1****u 1
Matt O'Rourke m****1@g****m 1
Reza Haghshenas r****s@g****m 1
Sergei Kozelko k****s@y****u 1
TanTsiChen 3****n 1
king-p3nguin k****a@g****p 1
paulsbrookes p****s@g****m 1
pulkin g****n@g****m 1
Committer Domains (Top 20 + Academic)

Issues and Pull Requests

Last synced: 4 months ago

All Time
  • Total issues: 139
  • Total pull requests: 50
  • Average time to close issues: 4 months
  • Average time to close pull requests: 4 months
  • Total issue authors: 75
  • Total pull request authors: 21
  • Average comments per issue: 2.59
  • Average comments per pull request: 2.58
  • Merged pull requests: 36
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 42
  • Pull requests: 13
  • Average time to close issues: 15 days
  • Average time to close pull requests: 2 days
  • Issue authors: 31
  • Pull request authors: 4
  • Average comments per issue: 1.52
  • Average comments per pull request: 0.77
  • Merged pull requests: 12
  • Bot issues: 0
  • Bot pull requests: 0
Top Authors
Issue Authors
  • jcmgray (16)
  • JustinS6626 (8)
  • ACE07-Sev (6)
  • kevinsung (6)
  • PietropaoloFrisoni (5)
  • pulkin (4)
  • paulsbrookes (4)
  • TanTsiChen (4)
  • thibxlv (3)
  • adamcallison (3)
  • sajjan02purdue (3)
  • cyasar (2)
  • garrison (2)
  • yourball (2)
  • GustavJaeger (2)
Pull Request Authors
  • adamcallison (9)
  • jcmgray (9)
  • kevinsung (7)
  • TanTsiChen (3)
  • AidanGG (3)
  • pulkin (2)
  • edenian (2)
  • juliendrapeau (2)
  • chris-n-self (1)
  • codacy-badger (1)
  • mlxd (1)
  • rezah (1)
  • iyanmv (1)
  • mattorourke17 (1)
  • yangcal (1)
Top Labels
Issue Labels
bug (32) enhancement (22) question (7) help wanted (1)
Pull Request Labels

Packages

  • Total packages: 2
  • Total downloads:
    • pypi 74,376 last-month
  • Total docker downloads: 305
  • Total dependent packages: 18
    (may contain duplicates)
  • Total dependent repositories: 29
    (may contain duplicates)
  • Total versions: 50
  • Total maintainers: 1
pypi.org: quimb

Quantum information and many-body library.

  • Versions: 33
  • Dependent Packages: 18
  • Dependent Repositories: 29
  • Downloads: 74,376 Last month
  • Docker Downloads: 305
Rankings
Dependent packages count: 0.8%
Average: 2.0%
Docker downloads count: 2.2%
Downloads: 2.2%
Dependent repos count: 2.7%
Maintainers (1)
Last synced: 4 months ago
proxy.golang.org: github.com/jcmgray/quimb
  • Versions: 17
  • Dependent Packages: 0
  • Dependent Repositories: 0
Rankings
Dependent packages count: 5.5%
Average: 5.6%
Dependent repos count: 5.8%
Last synced: 4 months ago

Dependencies

.github/workflows/pypi-release.yml actions
  • actions/checkout v3 composite
  • actions/download-artifact v3 composite
  • actions/setup-python v4 composite
  • actions/upload-artifact v3 composite
  • pypa/gh-action-pypi-publish v1.5.1 composite
.github/workflows/tests.yml actions
  • actions/checkout v3 composite
  • codecov/codecov-action v3 composite
  • mamba-org/provision-with-micromamba main composite
pyproject.toml pypi
setup.py pypi
  • autoray >=0.6.7
  • cotengra >=0.5.3
  • cytoolz >=0.8.0
  • numba >=0.39
  • numpy >=1.17
  • psutil >=4.3.1
  • scipy >=1.0.0
  • tqdm >=4