[](https://github.com/jcmgray/cotengra/actions/workflows/test.yml) [](https://codecov.io/gh/jcmgray/cotengra) [](https://app.codacy.com/gh/jcmgray/cotengra/dashboard?utm_source=gh&utm_medium=referral&utm_content=&utm_campaign=Badge_grade) [](https://cotengra.readthedocs.io) [](https://pypi.org/project/cotengra/) [](https://anaconda.org/conda-forge/cotengra) `cotengra` is a python library for contracting tensor networks or einsum expressions involving large numbers of tensors. The original accompanying paper is [***'Hyper-optimized tensor network contraction'***](https://quantum-journal.org/papers/q-2021-03-15-410/) - if this project is useful please consider citing this to encourage further development! Some of the key feautures of `cotengra` include: * drop-in ``einsum`` replacement * an explicit **contraction tree** object that can be flexibly built, modified and visualized * a **'hyper optimizer'** that samples trees while tuning the generating meta-paremeters * **dynamic slicing** for massive memory savings and parallelism * support for **hyper** edge tensor networks and thus arbitrary einsum equations * **paths** that can be supplied to [`numpy.einsum`](https://numpy.org/doc/stable/reference/generated/numpy.einsum.html), [`opt_einsum`](https://dgasmith.github.io/opt_einsum/), [`quimb`](https://quimb.readthedocs.io/en/latest/) among others * **performing contractions** with tensors from many libraries via [`autoray`](https://github.com/jcmgray/autoray), even if they don't provide einsum or tensordot but do have (batch) matrix multiplication --- Commits since [906f838](https://github.com/jcmgray/cotengra/commit/906f838d21840dc41c652efb63faf1f88c6113ee) added functionality first demonstrated in ['Classical Simulation of Quantum Supremacy Circuits'](https://arxiv.org/abs/2005.06787) - namely, contraction subtree reconfiguration and the interleaving of this with slicing - *dynamic slicing*. In the ``examples`` folder you can find notebooks reproducing (in terms of sliced contraction complexity) the results of that paper as well as ['Simulating the Sycamore quantum supremacy circuits'](https://arxiv.org/abs/2103.03074). Indeed, in collaboration with NVidia, ``cotengra`` & ``quimb`` have now been used for a state-of-the-art simulation of the Sycamore chip with ``cutensor`` on the Selene supercomputer, [producing a sample from a circuit of depth 20 in less than 10 minutes](https://blogs.nvidia.com/blog/2021/04/12/what-is-quantum-computing/). --- ```{toctree} :caption: Guides :maxdepth: 2 installation.md high-level-interface.ipynb basics.ipynb advanced.ipynb visualization.ipynb contraction.ipynb trees.ipynb index_examples.md ``` ```{toctree} :caption: Development :hidden: changelog.md GitHub Repository ```