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Article: Network-Initialized Monte Carlo Based on Generative Neural Networks

TitleNetwork-Initialized Monte Carlo Based on Generative Neural Networks
Authors
Issue Date2022
Citation
Chinese Physics Letters, 2022, v. 39, n. 5, article no. 050701 How to Cite?
AbstractWe design generative neural networks that generate Monte Carlo configurations with complete absence of autocorrelation from which only short Markov chains are needed before making measurements for physical observables, irrespective of the system locating at the classical critical point, fermionic Mott insulator, Dirac semimetal, or quantum critical point. We further propose a network-initialized Monte Carlo scheme based on such neural networks, which provides independent samplings and can accelerate the Monte Carlo simulations by significantly reducing the thermalization process. We demonstrate the performance of our approach on the two-dimensional Ising and fermion Hubbard models, expect that it can systematically speed up the Monte Carlo simulations especially for the very challenging many-electron problems.
Persistent Identifierhttp://hdl.handle.net/10722/330809
ISSN
2023 Impact Factor: 3.5
2023 SCImago Journal Rankings: 0.815
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorLu, Hongyu-
dc.contributor.authorLi, Chuhao-
dc.contributor.authorChen, Bin Bin-
dc.contributor.authorLi, Wei-
dc.contributor.authorQi, Yang-
dc.contributor.authorMeng, Zi Yang-
dc.date.accessioned2023-09-05T12:14:38Z-
dc.date.available2023-09-05T12:14:38Z-
dc.date.issued2022-
dc.identifier.citationChinese Physics Letters, 2022, v. 39, n. 5, article no. 050701-
dc.identifier.issn0256-307X-
dc.identifier.urihttp://hdl.handle.net/10722/330809-
dc.description.abstractWe design generative neural networks that generate Monte Carlo configurations with complete absence of autocorrelation from which only short Markov chains are needed before making measurements for physical observables, irrespective of the system locating at the classical critical point, fermionic Mott insulator, Dirac semimetal, or quantum critical point. We further propose a network-initialized Monte Carlo scheme based on such neural networks, which provides independent samplings and can accelerate the Monte Carlo simulations by significantly reducing the thermalization process. We demonstrate the performance of our approach on the two-dimensional Ising and fermion Hubbard models, expect that it can systematically speed up the Monte Carlo simulations especially for the very challenging many-electron problems.-
dc.languageeng-
dc.relation.ispartofChinese Physics Letters-
dc.titleNetwork-Initialized Monte Carlo Based on Generative Neural Networks-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1088/0256-307X/39/5/050701-
dc.identifier.scopuseid_2-s2.0-85131131425-
dc.identifier.volume39-
dc.identifier.issue5-
dc.identifier.spagearticle no. 050701-
dc.identifier.epagearticle no. 050701-
dc.identifier.eissn1741-3540-
dc.identifier.isiWOS:000795680200001-

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