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logo](https://github.com/jcmgray/quimb/blob/HEAD/docs/_static/logo-banner.png?raw=true)\n\n[![Tests](https://github.com/jcmgray/quimb/actions/workflows/tests.yml/badge.svg)](https://github.com/jcmgray/quimb/actions/workflows/tests.yml)\n[![Code Coverage](https://codecov.io/gh/jcmgray/quimb/branch/main/graph/badge.svg)](https://codecov.io/gh/jcmgray/quimb)\n[![Code Quality](https://app.codacy.com/project/badge/Grade/3c7462a3c45f41fd9d8f0a746a65c37c)](https://www.codacy.com/gh/jcmgray/quimb/dashboard?utm_source=github.com\u0026amp;utm_medium=referral\u0026amp;utm_content=jcmgray/quimb\u0026amp;utm_campaign=Badge_Grade)\n[![Documentation Status](https://readthedocs.org/projects/quimb/badge/?version=latest)](http://quimb.readthedocs.io/en/latest/?badge=latest)\n[![JOSS Paper](http://joss.theoj.org/papers/10.21105/joss.00819/status.svg)](https://doi.org/10.21105/joss.00819)\n[![PyPI](https://img.shields.io/pypi/v/quimb?color=teal)](https://pypi.org/project/quimb/)\n[![Anaconda-Server Badge](https://anaconda.org/conda-forge/quimb/badges/version.svg)](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 \u0026 TEBD\n\n![tensor pic](https://github.com/jcmgray/quimb/blob/HEAD/docs/_static/rand-tensor.svg?raw=true)\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![matrix pic](https://github.com/jcmgray/quimb/blob/HEAD/docs/_static/rand-herm-matrix.svg?raw=true)\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).","funding_links":[],"categories":["Quantum simulators"],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjcmgray%2Fquimb","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fjcmgray%2Fquimb","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjcmgray%2Fquimb/lists"}