Kernel-based quantum regressor models learning non-Markovianity
Autor(a) principal: | |
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Data de Publicação: | 2023 |
Outros Autores: | , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1103/PhysRevA.107.022402 http://hdl.handle.net/11449/246795 |
Resumo: | Quantum machine learning is a growing research field that aims to perform machine learning tasks assisted by a quantum computer. Kernel-based quantum machine learning models are paradigmatic examples where the kernel involves quantum states, and the Gram matrix is calculated from the overlap between these states. With the kernel at hand, a regular machine learning model is used for the learning process. In this paper we investigate the quantum support vector machine and quantum kernel ridge models to predict the degree of non-Markovianity of a quantum system. We perform digital quantum simulation of amplitude damping and phase damping channels to create our quantum data set. We elaborate on different kernel functions to map the data and kernel circuits to compute the overlap between quantum states. We show that our models deliver accurate predictions that are comparable with the fully classical models. |
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Repositório Institucional da UNESP |
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2946 |
spelling |
Kernel-based quantum regressor models learning non-MarkovianityQuantum machine learning is a growing research field that aims to perform machine learning tasks assisted by a quantum computer. Kernel-based quantum machine learning models are paradigmatic examples where the kernel involves quantum states, and the Gram matrix is calculated from the overlap between these states. With the kernel at hand, a regular machine learning model is used for the learning process. In this paper we investigate the quantum support vector machine and quantum kernel ridge models to predict the degree of non-Markovianity of a quantum system. We perform digital quantum simulation of amplitude damping and phase damping channels to create our quantum data set. We elaborate on different kernel functions to map the data and kernel circuits to compute the overlap between quantum states. We show that our models deliver accurate predictions that are comparable with the fully classical models.Centro de Óptica e Información Cuántica Universidad Mayor, Vicerrectoría de InvestigaciónEscuela Data Science Facultad de Ciencias Ingenería y Tecnología Universidad MayorUniversidad Mayor Vicerrectoría de InvestigaciónFaculdade de Ciências UNESP Universidade Estadual Paulista, SPDepartment of Physics Florida International UniversityUniversidad Bernardo O Higgins Santiago de ChileFaculdade de Ciências UNESP Universidade Estadual Paulista, SPUniversidad MayorVicerrectoría de InvestigaciónUniversidade Estadual Paulista (UNESP)Florida International UniversitySantiago de ChileTancara, DiegoDinani, Hossein T.Norambuena, ArielFanchini, Felipe F. [UNESP]Coto, Raúl2023-07-29T12:50:44Z2023-07-29T12:50:44Z2023-02-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://dx.doi.org/10.1103/PhysRevA.107.022402Physical Review A, v. 107, n. 2, 2023.2469-99342469-9926http://hdl.handle.net/11449/24679510.1103/PhysRevA.107.0224022-s2.0-85147735110Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengPhysical Review Ainfo:eu-repo/semantics/openAccess2023-07-29T12:50:44Zoai:repositorio.unesp.br:11449/246795Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T14:17:30.699296Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Kernel-based quantum regressor models learning non-Markovianity |
title |
Kernel-based quantum regressor models learning non-Markovianity |
spellingShingle |
Kernel-based quantum regressor models learning non-Markovianity Tancara, Diego |
title_short |
Kernel-based quantum regressor models learning non-Markovianity |
title_full |
Kernel-based quantum regressor models learning non-Markovianity |
title_fullStr |
Kernel-based quantum regressor models learning non-Markovianity |
title_full_unstemmed |
Kernel-based quantum regressor models learning non-Markovianity |
title_sort |
Kernel-based quantum regressor models learning non-Markovianity |
author |
Tancara, Diego |
author_facet |
Tancara, Diego Dinani, Hossein T. Norambuena, Ariel Fanchini, Felipe F. [UNESP] Coto, Raúl |
author_role |
author |
author2 |
Dinani, Hossein T. Norambuena, Ariel Fanchini, Felipe F. [UNESP] Coto, Raúl |
author2_role |
author author author author |
dc.contributor.none.fl_str_mv |
Universidad Mayor Vicerrectoría de Investigación Universidade Estadual Paulista (UNESP) Florida International University Santiago de Chile |
dc.contributor.author.fl_str_mv |
Tancara, Diego Dinani, Hossein T. Norambuena, Ariel Fanchini, Felipe F. [UNESP] Coto, Raúl |
description |
Quantum machine learning is a growing research field that aims to perform machine learning tasks assisted by a quantum computer. Kernel-based quantum machine learning models are paradigmatic examples where the kernel involves quantum states, and the Gram matrix is calculated from the overlap between these states. With the kernel at hand, a regular machine learning model is used for the learning process. In this paper we investigate the quantum support vector machine and quantum kernel ridge models to predict the degree of non-Markovianity of a quantum system. We perform digital quantum simulation of amplitude damping and phase damping channels to create our quantum data set. We elaborate on different kernel functions to map the data and kernel circuits to compute the overlap between quantum states. We show that our models deliver accurate predictions that are comparable with the fully classical models. |
publishDate |
2023 |
dc.date.none.fl_str_mv |
2023-07-29T12:50:44Z 2023-07-29T12:50:44Z 2023-02-01 |
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.107.022402 Physical Review A, v. 107, n. 2, 2023. 2469-9934 2469-9926 http://hdl.handle.net/11449/246795 10.1103/PhysRevA.107.022402 2-s2.0-85147735110 |
url |
http://dx.doi.org/10.1103/PhysRevA.107.022402 http://hdl.handle.net/11449/246795 |
identifier_str_mv |
Physical Review A, v. 107, n. 2, 2023. 2469-9934 2469-9926 10.1103/PhysRevA.107.022402 2-s2.0-85147735110 |
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.source.none.fl_str_mv |
Scopus 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_ |
1808128341152104448 |