Machine learning, quantum chaos, and pseudorandom evolution
Autor(a) principal: | |
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Data de Publicação: | 2020 |
Outros Autores: | |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1103/PhysRevA.101.052338 http://hdl.handle.net/11449/196914 |
Resumo: | By modeling quantum chaotic dynamics with ensembles of random operators, we explore how machine learning algorithms can be used to detect pseudorandom behavior in qubit systems. We analyze samples consisting of pieces of correlation functions and find that machine learning algorithms are capable of determining the degree of pseudorandomness which a system is subject to in a precise sense. This is done without computing any correlators explicitly. Interestingly, even samples drawn from two-point functions are found to be sufficient to solve this classification problem. This presents the possibility of using deep learning algorithms to explore late time behavior in chaotic quantum systems which have been inaccessible to simulation. |
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Repositório Institucional da UNESP |
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Machine learning, quantum chaos, and pseudorandom evolutionBy modeling quantum chaotic dynamics with ensembles of random operators, we explore how machine learning algorithms can be used to detect pseudorandom behavior in qubit systems. We analyze samples consisting of pieces of correlation functions and find that machine learning algorithms are capable of determining the degree of pseudorandomness which a system is subject to in a precise sense. This is done without computing any correlators explicitly. Interestingly, even samples drawn from two-point functions are found to be sufficient to solve this classification problem. This presents the possibility of using deep learning algorithms to explore late time behavior in chaotic quantum systems which have been inaccessible to simulation.Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Univ Calif Davis, Ctr Quantum Math & Phys, Dept Phys, Davis, CA 95616 USAUniv Estadual Paulista, Sao Paulo State Univ, Inst Theoret Phys IFT, R Dr Bento T Ferraz 271, BR-01140070 Sao Paulo, SP, BrazilUniv Estadual Paulista, Sao Paulo State Univ, Inst Theoret Phys IFT, R Dr Bento T Ferraz 271, BR-01140070 Sao Paulo, SP, BrazilCAPES: 001CNPq: 146086/2015-5Amer Physical SocUniv Calif DavisUniversidade Estadual Paulista (Unesp)Alves, Daniel W. F. [UNESP]Flynn, Michael O.2020-12-10T20:00:15Z2020-12-10T20:00:15Z2020-05-27info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article7http://dx.doi.org/10.1103/PhysRevA.101.052338Physical Review A. College Pk: Amer Physical Soc, v. 101, n. 5, 7 p., 2020.1050-2947http://hdl.handle.net/11449/19691410.1103/PhysRevA.101.052338WOS:000535667400004Web of Sciencereponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengPhysical Review Ainfo:eu-repo/semantics/openAccess2021-10-23T10:11:08Zoai:repositorio.unesp.br:11449/196914Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T19:14:46.791813Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Machine learning, quantum chaos, and pseudorandom evolution |
title |
Machine learning, quantum chaos, and pseudorandom evolution |
spellingShingle |
Machine learning, quantum chaos, and pseudorandom evolution Alves, Daniel W. F. [UNESP] |
title_short |
Machine learning, quantum chaos, and pseudorandom evolution |
title_full |
Machine learning, quantum chaos, and pseudorandom evolution |
title_fullStr |
Machine learning, quantum chaos, and pseudorandom evolution |
title_full_unstemmed |
Machine learning, quantum chaos, and pseudorandom evolution |
title_sort |
Machine learning, quantum chaos, and pseudorandom evolution |
author |
Alves, Daniel W. F. [UNESP] |
author_facet |
Alves, Daniel W. F. [UNESP] Flynn, Michael O. |
author_role |
author |
author2 |
Flynn, Michael O. |
author2_role |
author |
dc.contributor.none.fl_str_mv |
Univ Calif Davis Universidade Estadual Paulista (Unesp) |
dc.contributor.author.fl_str_mv |
Alves, Daniel W. F. [UNESP] Flynn, Michael O. |
description |
By modeling quantum chaotic dynamics with ensembles of random operators, we explore how machine learning algorithms can be used to detect pseudorandom behavior in qubit systems. We analyze samples consisting of pieces of correlation functions and find that machine learning algorithms are capable of determining the degree of pseudorandomness which a system is subject to in a precise sense. This is done without computing any correlators explicitly. Interestingly, even samples drawn from two-point functions are found to be sufficient to solve this classification problem. This presents the possibility of using deep learning algorithms to explore late time behavior in chaotic quantum systems which have been inaccessible to simulation. |
publishDate |
2020 |
dc.date.none.fl_str_mv |
2020-12-10T20:00:15Z 2020-12-10T20:00:15Z 2020-05-27 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.1103/PhysRevA.101.052338 Physical Review A. College Pk: Amer Physical Soc, v. 101, n. 5, 7 p., 2020. 1050-2947 http://hdl.handle.net/11449/196914 10.1103/PhysRevA.101.052338 WOS:000535667400004 |
url |
http://dx.doi.org/10.1103/PhysRevA.101.052338 http://hdl.handle.net/11449/196914 |
identifier_str_mv |
Physical Review A. College Pk: Amer Physical Soc, v. 101, n. 5, 7 p., 2020. 1050-2947 10.1103/PhysRevA.101.052338 WOS:000535667400004 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Physical Review A |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
7 |
dc.publisher.none.fl_str_mv |
Amer Physical Soc |
publisher.none.fl_str_mv |
Amer Physical Soc |
dc.source.none.fl_str_mv |
Web of Science reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
|
_version_ |
1808129040525033472 |