[](https://github.com/jcmgray/quimb/actions/workflows/tests.yml)
[](https://codecov.io/gh/jcmgray/quimb)
[](https://www.codacy.com/gh/jcmgray/quimb/dashboard?utm_source=github.com&utm_medium=referral&utm_content=jcmgray/quimb&utm_campaign=Badge_Grade)
[](http://quimb.readthedocs.io/en/latest/?badge=latest)
[](https://doi.org/10.21105/joss.00819)
[](https://pypi.org/project/quimb/)
[](https://anaconda.org/conda-forge/quimb)
[`quimb`](https://github.com/jcmgray/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](https://github.com/jcmgray/quimb), and docs are hosted on [readthedocs](http://quimb.readthedocs.io/en/latest/). 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](https://cotengra.readthedocs.io), optimize and draw networks
- use various backend array libraries such as [jax](https://jax.readthedocs.io) and [torch](https://pytorch.org/) via [autoray](https://github.com/jcmgray/autoray/)
- run specific MPS, PEPS, MERA and quantum circuit algorithms, such as DMRG & TEBD
---
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](https://slepc.upv.es/)
- compute various quantities including entanglement measures
- take advantage of [numba](https://numba.pydata.org) accelerations
- stochastically estimate $\mathrm{Tr}f(X)$ quantities
---
The **full documentation** can be found at: [quimb.readthedocs.io](https://quimb.readthedocs.io). Contributions of any sort are very welcome - please see the [contributing guide](https://github.com/jcmgray/quimb/blob/main/.github/CONTRIBUTING.md). [Issues](https://github.com/jcmgray/quimb/issues) and [pull requests](https://github.com/jcmgray/quimb/pulls) are hosted on [github](https://github.com/jcmgray/quimb). For other questions and suggestions, please use the [discussions page](https://github.com/jcmgray/quimb/discussions).
Raw data
{
"_id": null,
"home_page": null,
"name": "quimb",
"maintainer": null,
"docs_url": null,
"requires_python": ">=3.9",
"maintainer_email": null,
"keywords": "dmrg, mera, networks, peps, physics, quantum, tebd, tensor, tensors",
"author": null,
"author_email": "Johnnie Gray <johnniemcgray@gmail.com>",
"download_url": "https://files.pythonhosted.org/packages/db/f6/71786ef5898856fffdbdb4d650d3f81bf3e1cfa92e61405c3858a59c696f/quimb-1.11.2.tar.gz",
"platform": null,
"description": "\n\n[](https://github.com/jcmgray/quimb/actions/workflows/tests.yml)\n[](https://codecov.io/gh/jcmgray/quimb)\n[](https://www.codacy.com/gh/jcmgray/quimb/dashboard?utm_source=github.com&utm_medium=referral&utm_content=jcmgray/quimb&utm_campaign=Badge_Grade)\n[](http://quimb.readthedocs.io/en/latest/?badge=latest)\n[](https://doi.org/10.21105/joss.00819)\n[](https://pypi.org/project/quimb/)\n[](https://anaconda.org/conda-forge/quimb)\n\n[`quimb`](https://github.com/jcmgray/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](https://github.com/jcmgray/quimb), and docs are hosted on [readthedocs](http://quimb.readthedocs.io/en/latest/). Functionality is split in two:\n\n---\n\nThe `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:\n\n- construct and manipulate arbitrary (hyper) graphs of tensor networks\n- automatically [contract](https://cotengra.readthedocs.io), optimize and draw networks\n- use various backend array libraries such as [jax](https://jax.readthedocs.io) and [torch](https://pytorch.org/) via [autoray](https://github.com/jcmgray/autoray/)\n- run specific MPS, PEPS, MERA and quantum circuit algorithms, such as DMRG & TEBD\n\n\n\n---\n\nThe 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:\n\n- construct operators in complicated tensor spaces\n- find groundstates, excited states and do time evolutions, including with [slepc](https://slepc.upv.es/)\n- compute various quantities including entanglement measures\n- take advantage of [numba](https://numba.pydata.org) accelerations\n- stochastically estimate $\\mathrm{Tr}f(X)$ quantities\n\n\n\n---\n\nThe **full documentation** can be found at: [quimb.readthedocs.io](https://quimb.readthedocs.io). Contributions of any sort are very welcome - please see the [contributing guide](https://github.com/jcmgray/quimb/blob/main/.github/CONTRIBUTING.md). [Issues](https://github.com/jcmgray/quimb/issues) and [pull requests](https://github.com/jcmgray/quimb/pulls) are hosted on [github](https://github.com/jcmgray/quimb). For other questions and suggestions, please use the [discussions page](https://github.com/jcmgray/quimb/discussions).",
"bugtrack_url": null,
"license": "Apache-2.0",
"summary": "Quantum information and many-body library.",
"version": "1.11.2",
"project_urls": {
"Changelog": "https://quimb.readthedocs.io/en/latest/changelog.html",
"Documentation": "https://quimb.readthedocs.io/",
"Issues": "https://github.com/jcmgray/quimb/issues",
"Repository": "https://github.com/jcmgray/quimb/"
},
"split_keywords": [
"dmrg",
" mera",
" networks",
" peps",
" physics",
" quantum",
" tebd",
" tensor",
" tensors"
],
"urls": [
{
"comment_text": "",
"digests": {
"blake2b_256": "4cbecc32aaf468c5856e0c738ae7b2e494d12d50db3906c751834ca5f0e157fb",
"md5": "7e8e3b17bf3a378c3a09203bbfd76968",
"sha256": "5c8a66311f54f3894e39f23d83cef4537cc6d41011779733ca303f3e5f09c7b8"
},
"downloads": -1,
"filename": "quimb-1.11.2-py3-none-any.whl",
"has_sig": false,
"md5_digest": "7e8e3b17bf3a378c3a09203bbfd76968",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": ">=3.9",
"size": 2077705,
"upload_time": "2025-07-31T00:25:24",
"upload_time_iso_8601": "2025-07-31T00:25:24.383561Z",
"url": "https://files.pythonhosted.org/packages/4c/be/cc32aaf468c5856e0c738ae7b2e494d12d50db3906c751834ca5f0e157fb/quimb-1.11.2-py3-none-any.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "dbf671786ef5898856fffdbdb4d650d3f81bf3e1cfa92e61405c3858a59c696f",
"md5": "f711edf83f2ce302167b5faf331e7a9a",
"sha256": "12546651f714e4ff3adb53dc7b620b4c4eb3fd8d8aafad0f92ac4cc04f03440c"
},
"downloads": -1,
"filename": "quimb-1.11.2.tar.gz",
"has_sig": false,
"md5_digest": "f711edf83f2ce302167b5faf331e7a9a",
"packagetype": "sdist",
"python_version": "source",
"requires_python": ">=3.9",
"size": 10142651,
"upload_time": "2025-07-31T00:25:26",
"upload_time_iso_8601": "2025-07-31T00:25:26.477606Z",
"url": "https://files.pythonhosted.org/packages/db/f6/71786ef5898856fffdbdb4d650d3f81bf3e1cfa92e61405c3858a59c696f/quimb-1.11.2.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2025-07-31 00:25:26",
"github": true,
"gitlab": false,
"bitbucket": false,
"codeberg": false,
"github_user": "jcmgray",
"github_project": "quimb",
"travis_ci": false,
"coveralls": false,
"github_actions": true,
"lcname": "quimb"
}
JSON
