SeeMPS: A Python-based Matrix Product State and Tensor Train Library

Abstract:We introduce SeeMPS, a Python library dedicated to implementing tensor network algorithms based on the well-known Matrix Product States (MPS) and Quantized Tensor Train (QTT) formalisms. SeeMPS is implemented as a complete finite precision linear algebra package where exponentially large vector spaces are compressed using the MPS/TT formalism. It enables both low-level operations, such as vector addition, linear transformations, and Hadamard products, as well as high-level algorithms, including the approximation of linear equations, eigenvalue computations, and exponentially efficient Fourier transforms. This library can be used for traditional quantum many-body physics applications and also for quantum-inspired numerical analysis problems, such as solving PDEs, interpolating and integrating multidimensional functions, sampling multivariate probability distributions, etc.

26 pages, 9 figures, 2 tables. Submitted to Computer Physics Communications

Quantum Physics (quant-ph); Numerical Analysis (math.NA)

arXiv:2601.16734 [quant-ph]

(or arXiv:2601.16734v2 [quant-ph] for this version)

https://doi.org/10.48550/arXiv.2601.16734 Focus to learn more arXiv-issued DOI via DataCite

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