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YASTN: Yet another symmetric tensor networks; A Python library for abelian symmetric tensor network calculations
by Marek M. Rams, Gabriela Wójtowicz, Aritra Sinha, Juraj Hasik
Submission summary
| Authors (as registered SciPost users): | Juraj Hasik |
| Submission information | |
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| Preprint Link: | scipost_202406_00058v1 (pdf) |
| Code repository: | https://github.com/yastn/yastn |
| Date submitted: | 2024-06-27 13:58 |
| Submitted by: | Hasik, Juraj |
| Submitted to: | SciPost Physics Codebases |
| Ontological classification | |
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| Academic field: | Physics |
| Specialties: |
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| Approaches: | Theoretical, Computational |
Abstract
We present an open-source tensor network Python library for quantum many-body simulations. At its core is an abelian-symmetric tensor, implemented as a sparse block structure managed by a logical layer on top of a dense multi-dimensional array backend. This serves as the basis for higher-level tensor network algorithms, operating on matrix product states and projected entangled pair states. An appropriate backend, such as PyTorch, gives direct access to automatic differentiation (AD) for cost-function gradient calculations and execution on GPU and other supported accelerators. We show the library performance in simulations with infinite projected entangled-pair states, such as finding the ground states with AD and simulating thermal states of the Hubbard model via imaginary time evolution. For these challenging examples, we identify and quantify sources of the numerical advantage exploited by the symmetric-tensor implementation.