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quimb is an easy but fast python library for quantum information and many-body calculations, including with tensor networks. The code is hosted on github, do please submit any issues or pull requests there. It is also thoroughly unit-tested and the tests might be the best place to look for detailed documentation.

The core quimb module:

• Uses straight numpy and scipy.sparse matrices as quantum objects
• Accelerates and parallelizes many operations using numba.
• Makes it easy to construct operators in large tensor spaces (e.g. 2D lattices)
• Uses efficient methods to compute various quantities including entanglement measures
• Has many built-in states and operators, including those based on fast, parallel random number generation
• Can perform evolutions with several methods, computing quantities on the fly
• Has an optional slepc4py interface for easy distributed (MPI) linear algebra. This can massively increase the performance when seeking, for example, mid-spectrum eigenstates

The tensor network submodule quimb.tensor:

• Uses a geometry free representation of tensor networks
• Uses opt_einsum to find efficient contraction orders for hundreds or thousands of tensors
• Can perform those contractions on various backends, including with a GPU
• Can plot any network, color-coded, with bond size represented
• Can treat any network as a scipy LinearOperator, allowing many decompositions
• Can perform DMRG1, DMRG2 and DMRGX, in matrix product state language
• Has tools to efficiently address periodic problems (transfer matrix compression and pseudo-orthogonalization)
• Can perform MPS time evolutions with TEBD
• Can optimize arbitrary tensor networks with tensorflow, pytorch, jax or autograd

The full documentation can be found at: http://quimb.readthedocs.io/en/latest/. Contributions of any sort are very welcome - please see the contributing guide. Issues and pull requests are hosted on github. For other questions and suggestions, please use the dicusssions page.