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https://github.com/jcmgray/quimb

A python library for quantum information and many-body calculations including tensor networks.
https://github.com/jcmgray/quimb

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

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

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README 

        
![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:



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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)



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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)



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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).