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

• URL: http://arxiv.org/abs/2601.16734v1
• Date: Fri, 23 Jan 2026 13:31:16 GMT
• Title: SeeMPS: A Python-based Matrix Product State and Tensor Train Library
• Authors: Paula García-Molina, Juan José Rodríguez-Aldavero, Jorge Gidi, Juan José García-Ripoll,
• Abstract summary: SeeMPS is a Python library dedicated to implementing tensor network algorithms based on the Matrix Product States (MPS) and Quantized Train (QTT) formalisms.<n>This library can be used for traditional quantum many-body physics applications and also for quantum-inspired numerical analysis problems.
• Score: 1.0499611180329804
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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