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...

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Authors: Sáiz, Álvaro, García Ramos, José Enrique, Arias Carrasco, José Miguel, Lamata, Lucas, Pérez Fernández, Pedro
Format: article
Publication Date:2022
Country:España
Institution:Universidad de Huelva (UHU)
Repository:Arias Montano. Repositorio Institucional de la Universidad de Huelva
Language:English
OAI Identifier:oai:ariasmontano.uhu.es:10272/21372
Online Access:https://hdl.handle.net/10272/21372
Access Level:Open access
Keyword:Quantum simulation
Machine learning
Nuclear many-body theory
Phase diagrams
Quantum phase transitions
22 Física
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spelling Digital quantum simulation of an extended Agassi model: Using machine learning to disentangle its phase-diagramSáiz, ÁlvaroGarcía Ramos, José EnriqueArias Carrasco, José MiguelLamata, LucasPérez Fernández, PedroQuantum simulationMachine learningNuclear many-body theoryPhase diagramsQuantum phase transitions22 FísicaA 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-American Physical Society20222022-01-0120222022-01-01journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/10272/21372reponame:Arias Montano. Repositorio Institucional de la Universidad de Huelvainstname:Universidad de Huelva (UHU)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2Atribución-NoComercial-SinDerivadas 3.0 Españahttp://creativecommons.org/licenses/by-nc-nd/3.0/es/info:eu-repo/semantics/openAccessoai:ariasmontano.uhu.es:10272/213722026-06-02T14:58:11Z
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, Álvaro
Quantum simulation
Machine learning
Nuclear many-body theory
Phase diagrams
Quantum phase transitions
22 Física
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, Álvaro
García Ramos, José Enrique
Arias Carrasco, José Miguel
Lamata, Lucas
Pérez Fernández, Pedro
author Sáiz, Álvaro
author_facet Sáiz, Álvaro
García Ramos, José Enrique
Arias Carrasco, José Miguel
Lamata, Lucas
Pérez Fernández, Pedro
author_role author
author2 García Ramos, José Enrique
Arias Carrasco, José Miguel
Lamata, Lucas
Pérez Fernández, Pedro
author2_role author
author
author
author
dc.contributor.none.fl_str_mv
dc.subject.none.fl_str_mv Quantum simulation
Machine learning
Nuclear many-body theory
Phase diagrams
Quantum phase transitions
22 Física
topic Quantum simulation
Machine learning
Nuclear many-body theory
Phase diagrams
Quantum phase transitions
22 Física
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
2022-01-01
2022
2022-01-01
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/10272/21372
url https://hdl.handle.net/10272/21372
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Atribución-NoComercial-SinDerivadas 3.0 España
http://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Atribución-NoComercial-SinDerivadas 3.0 España
http://creativecommons.org/licenses/by-nc-nd/3.0/es/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 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:Arias Montano. Repositorio Institucional de la Universidad de Huelva
instname:Universidad de Huelva (UHU)
instname_str Universidad de Huelva (UHU)
reponame_str Arias Montano. Repositorio Institucional de la Universidad de Huelva
collection Arias Montano. Repositorio Institucional de la Universidad de Huelva
repository.name.fl_str_mv
repository.mail.fl_str_mv
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