Quantum information and many-body library.
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 are probably the best place to look for detailed documentation.
The core quimb module:
- Uses numpy and scipy.sparse matrices as quantum objects
- Accelerates many operations using numba and numexpr
- Makes it easy to construct operators in large tensor spaces (e.g. 2D lattices)
- Uses efficient methods to compute various quantities including entanglement measures
- Can generate a variety of random states and operators
- 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 of tensors, and perform those contractions potentially on the 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
The full documentation can be found at: http://quimb.readthedocs.io/en/latest/.
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