Digital quantum simulation of an extended Agassi model: Using machine learning to disentangle its phase-diagram

A digital quantum simulation for the extended Agassi model is proposed using a quantum platform with eight trapped ions. The extended Agassi model is an analytically solvable model including both short range pairing and long range monopole-monopole interactions with applications in nuclear physics a...

Descripción completa

Detalles Bibliográficos
Autores: Sáiz Castillo, Álvaro, García Ramos, José Enrique, Arias Carrasco, José Miguel, Lamata Manuel, Lucas, Pérez Fernández, Pedro
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2022
País:España
Institución:Universidad de Sevilla (US)
Repositorio:idUS. Depósito de Investigación de la Universidad de Sevilla
OAI Identifier:oai:idus.us.es:11441/140805
Acceso en línea:https://hdl.handle.net/11441/140805
https://doi.org/10.1103/physrevc.106.064322
Access Level:acceso abierto
id ES_0d0a9c8b3dcebc41d051bf762cd54c06
oai_identifier_str oai:idus.us.es:11441/140805
network_acronym_str ES
network_name_str España
repository_id_str
spelling Digital quantum simulation of an extended Agassi model: Using machine learning to disentangle its phase-diagramSáiz Castillo, ÁlvaroGarcía Ramos, José EnriqueArias Carrasco, José MiguelLamata Manuel, LucasPérez Fernández, PedroA digital quantum simulation for the extended Agassi model is proposed using a quantum platform with eight trapped ions. The extended Agassi model is an analytically solvable model including both short range pairing and long range monopole-monopole interactions with applications in nuclear physics and in other many-body systems. In addition, it owns a rich phase diagram with different phases and the corresponding phase transition surfaces. The aim of this work is twofold: on one hand, to propose a quantum simulation of the model at the present limits of the trapped ions facilities and, on the other hand, to show how to use a machine learning algorithm on top of the quantum simulation to accurately determine the phase of the system. Concerning the quantum simulation, this proposal is scalable with polynomial resources to larger Agassi systems. Digital quantum simulations of nuclear physics models assisted by machine learning may enable one to outperform the fastest classical computers in determining fundamental aspects of nuclear matter.Junta de Andalucía P20-00617, P20-00764, P20-01247, UHU-1262561 and US-1380840MCIN/AEI PGC2018-095113-B-I00, PID2019-104002GBC21, PID2019-104002GB-C22, and PID2020-114687GBI00ERDF/MINECO Project No. UNHU-15CE-2848American Physical SocietyFísica Atómica, Molecular y NuclearFísica Aplicada IIIMinisterio de Economía y Competitividad (MINECO). EspañaMinisterio de Ciencia e Innovación (MICIN). EspañaJunta de AndalucíaEuropean Commission (EC). Fondo Europeo de Desarrollo Regional (FEDER)2022info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfapplication/pdfhttps://hdl.handle.net/11441/140805https://doi.org/10.1103/physrevc.106.064322reponame:idUS. Depósito de Investigación de la Universidad de Sevillainstname:Universidad de Sevilla (US)InglésPhysical Review C, 106 (6), 064322.PGC2018-095113-B-I00PID2019-104002GBC21PID2019-104002GB-C22PID2020-114687GBI00P20-00617P20-00764P20-01247UHU-1262561US-1380840https://dx.doi.org/10.1103/physrevc.106.064322info:eu-repo/semantics/openAccessoai:idus.us.es:11441/1408052026-06-17T12:51:07Z
dc.title.none.fl_str_mv Digital quantum simulation of an extended Agassi model: Using machine learning to disentangle its phase-diagram
title Digital quantum simulation of an extended Agassi model: Using machine learning to disentangle its phase-diagram
spellingShingle Digital quantum simulation of an extended Agassi model: Using machine learning to disentangle its phase-diagram
Sáiz Castillo, Álvaro
title_short Digital quantum simulation of an extended Agassi model: Using machine learning to disentangle its phase-diagram
title_full Digital quantum simulation of an extended Agassi model: Using machine learning to disentangle its phase-diagram
title_fullStr Digital quantum simulation of an extended Agassi model: Using machine learning to disentangle its phase-diagram
title_full_unstemmed Digital quantum simulation of an extended Agassi model: Using machine learning to disentangle its phase-diagram
title_sort Digital quantum simulation of an extended Agassi model: Using machine learning to disentangle its phase-diagram
dc.creator.none.fl_str_mv Sáiz Castillo, Álvaro
García Ramos, José Enrique
Arias Carrasco, José Miguel
Lamata Manuel, Lucas
Pérez Fernández, Pedro
author Sáiz Castillo, Álvaro
author_facet Sáiz Castillo, Álvaro
García Ramos, José Enrique
Arias Carrasco, José Miguel
Lamata Manuel, Lucas
Pérez Fernández, Pedro
author_role author
author2 García Ramos, José Enrique
Arias Carrasco, José Miguel
Lamata Manuel, Lucas
Pérez Fernández, Pedro
author2_role author
author
author
author
dc.contributor.none.fl_str_mv Física Atómica, Molecular y Nuclear
Física Aplicada III
Ministerio de Economía y Competitividad (MINECO). España
Ministerio de Ciencia e Innovación (MICIN). España
Junta de Andalucía
European Commission (EC). Fondo Europeo de Desarrollo Regional (FEDER)
description A digital quantum simulation for the extended Agassi model is proposed using a quantum platform with eight trapped ions. The extended Agassi model is an analytically solvable model including both short range pairing and long range monopole-monopole interactions with applications in nuclear physics and in other many-body systems. In addition, it owns a rich phase diagram with different phases and the corresponding phase transition surfaces. The aim of this work is twofold: on one hand, to propose a quantum simulation of the model at the present limits of the trapped ions facilities and, on the other hand, to show how to use a machine learning algorithm on top of the quantum simulation to accurately determine the phase of the system. Concerning the quantum simulation, this proposal is scalable with polynomial resources to larger Agassi systems. Digital quantum simulations of nuclear physics models assisted by machine learning may enable one to outperform the fastest classical computers in determining fundamental aspects of nuclear matter.
publishDate 2022
dc.date.none.fl_str_mv 2022
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/11441/140805
https://doi.org/10.1103/physrevc.106.064322
url https://hdl.handle.net/11441/140805
https://doi.org/10.1103/physrevc.106.064322
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Physical Review C, 106 (6), 064322.
PGC2018-095113-B-I00
PID2019-104002GBC21
PID2019-104002GB-C22
PID2020-114687GBI00
P20-00617
P20-00764
P20-01247
UHU-1262561
US-1380840
https://dx.doi.org/10.1103/physrevc.106.064322
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv American Physical Society
publisher.none.fl_str_mv American Physical Society
dc.source.none.fl_str_mv reponame:idUS. Depósito de Investigación de la Universidad de Sevilla
instname:Universidad de Sevilla (US)
instname_str Universidad de Sevilla (US)
reponame_str idUS. Depósito de Investigación de la Universidad de Sevilla
collection idUS. Depósito de Investigación de la Universidad de Sevilla
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869403317663170560
score 15,301603

Similares en LA Referencia