# Welcome to quimb's documentation! [](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: ::::{grid} 2 :::{grid-item-card} Tensor module :img-bottom: _static/rand-tensor.svg 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 ::: :::{grid-item-card} Matrix module :img-bottom: _static/rand-herm-matrix.svg 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 ::: :::: (guides)= ## User Guide The following guides give a basic introduction to the various parts: ```{toctree} :caption: Guides :maxdepth: 1 installation ``` ```{toctree} :maxdepth: 2 index_tn index_matrix ``` (examples)= ## Examples The following examples, generated from the notebooks in ``docs/examples``, demonstrate some more advanced features or complete usage: ```{toctree} :maxdepth: 2 index_examples ``` (citing)= ## Citing ``quimb`` is published in the Journal of Open Source Software [here](https://doi.org/10.21105/joss.00819) - if it's ever useful in research please consider supporting development by citing it! ```latex @article{gray2018quimb, title={quimb: a python library for quantum information and many-body calculations}, author={Gray, Johnnie}, journal={Journal of Open Source Software}, year = {2018}, volume={3}, number={29}, pages={819}, doi={10.21105/joss.00819}, } ``` (development)= ## Notes Development notes, including on contributing to ``quimb`` and release details can be found below: ```{toctree} :caption: Development changelog develop ``` ```{toctree} :hidden: GitHub Repository ```