Machine learning, quantum chaos, and pseudorandom evolution

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MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversity of California-
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.creatorAlves, Daniel W.F.-
Autor(es): dc.creatorFlynn, Michael O.-
Data de aceite: dc.date.accessioned2025-08-21T19:34:10Z-
Data de disponibilização: dc.date.available2025-08-21T19:34:10Z-
Data de envio: dc.date.issued2022-04-29-
Data de envio: dc.date.issued2022-04-29-
Data de envio: dc.date.issued2020-05-01-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1103/PhysRevA.101.052338-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/228807-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/228807-
Descrição: dc.descriptionBy 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.-
Descrição: dc.descriptionCenter for Quantum Mathematics and Physics Department of Physics University of California-
Descrição: dc.descriptionUniversidade Estadual Paulista Sao Paulo State University Institute for Theoretical Physics (IFT), R. Dr. Bento T. Ferraz 271, Bl. II-
Descrição: dc.descriptionUniversidade Estadual Paulista Sao Paulo State University Institute for Theoretical Physics (IFT), R. Dr. Bento T. Ferraz 271, Bl. II-
Idioma: dc.languageen-
Relação: dc.relationPhysical Review A-
???dc.source???: dc.sourceScopus-
Título: dc.titleMachine learning, quantum chaos, and pseudorandom evolution-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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