quimb


Namequimb JSON
Version 1.8.4 PyPI version JSON
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home_pagehttp://quimb.readthedocs.io
SummaryQuantum information and many-body library.
upload_time2024-07-20 21:02:04
maintainerNone
docs_urlNone
authorJohnnie Gray
requires_python>=3.8
licenseApache
keywords quantum physics tensor networks tensors dmrg tebd
VCS
bugtrack_url
requirements No requirements were recorded.
Travis-CI No Travis.
coveralls test coverage No coveralls.
            ![quimb logo](https://github.com/jcmgray/quimb/blob/HEAD/docs/_static/logo-banner.png?raw=true)

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[`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

![tensor pic](https://github.com/jcmgray/quimb/blob/HEAD/docs/_static/rand-tensor.svg?raw=true)

---

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

![matrix pic](https://github.com/jcmgray/quimb/blob/HEAD/docs/_static/rand-herm-matrix.svg?raw=true)

---

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).

            

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