YASTN: Yet another symmetric tensor networks; A Python library for abelian symmetric tensor network calculations
Fuente:
arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866915180755025920 |
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| author | Rams, Marek M. Wójtowicz, Gabriela Sinha, Aritra Hasik, Juraj |
| author_facet | Rams, Marek M. Wójtowicz, Gabriela Sinha, Aritra Hasik, Juraj |
| contents | 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 logical layer on top of dense multi-dimensional array backend. This serves as the basis for higher-level tensor networks algorithms, operating on matrix product states and projected entangled pair states, implemented here. Using appropriate backend, such as PyTorch, gives direct access to automatic differentiation (AD) for cost-function gradient calculations and execution on GPUs or other supported accelerators. We show the library performance in simulations with infinite projected entangled-pair states, such as finding the ground states with AD, or simulating thermal states of the Hubbard model via imaginary time evolution. We quantify sources of performance gains in those challenging examples allowed by utilizing symmetries. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_12196 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | YASTN: Yet another symmetric tensor networks; A Python library for abelian symmetric tensor network calculations Rams, Marek M. Wójtowicz, Gabriela Sinha, Aritra Hasik, Juraj Strongly Correlated Electrons Computational Physics Quantum Physics 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 logical layer on top of dense multi-dimensional array backend. This serves as the basis for higher-level tensor networks algorithms, operating on matrix product states and projected entangled pair states, implemented here. Using appropriate backend, such as PyTorch, gives direct access to automatic differentiation (AD) for cost-function gradient calculations and execution on GPUs or other supported accelerators. We show the library performance in simulations with infinite projected entangled-pair states, such as finding the ground states with AD, or simulating thermal states of the Hubbard model via imaginary time evolution. We quantify sources of performance gains in those challenging examples allowed by utilizing symmetries. |
| title | YASTN: Yet another symmetric tensor networks; A Python library for abelian symmetric tensor network calculations |
| topic | Strongly Correlated Electrons Computational Physics Quantum Physics |
| url | https://arxiv.org/abs/2405.12196 |