Taking advantage of noise in quantum reservoir computing

Autores
Domingo, L.; Carlo, Gabriel Gustavo; Borondo, F.
Año de publicación
2023
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
The biggest challenge that quantum computing and quantum machine learning are currently facingis the presence of noise in quantum devices. As a result, big efforts have been put into correctingor mitigating the induced errors. But, can these two fields benefit from noise? Surprisingly, wedemonstrate that under some circumstances, quantum noise can be used to improve the performanceof quantum reservoir computing, a prominent and recent quantum machine learning algorithm.Our results show that the amplitude damping noise can be beneficial to machine learning, whilethe depolarizing and phase damping noises should be prioritized for correction. This critical resultsheds new light into the physical mechanisms underlying quantum devices, providing solid practicalprescriptions for a successful implementation of quantum information processing in nowadayshardware.
Fil: Domingo, L.. Instituto de Ciencias Matemáticas; España. Universidad Autónoma de Madrid; España. Universidad Politécnica de Madrid; España
Fil: Carlo, Gabriel Gustavo. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Comisión Nacional de Energía Atómica; Argentina
Fil: Borondo, F.. Universidad Autónoma de Madrid; España
Materia
Reservoir Computing
Quantum Computation
Quantum Machine Learning
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/232961
Acceder
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repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Taking advantage of noise in quantum reservoir computingDomingo, L.Carlo, Gabriel GustavoBorondo, F.Reservoir ComputingQuantum ComputationQuantum Machine Learninghttps://purl.org/becyt/ford/1.3https://purl.org/becyt/ford/1The biggest challenge that quantum computing and quantum machine learning are currently facingis the presence of noise in quantum devices. As a result, big efforts have been put into correctingor mitigating the induced errors. But, can these two fields benefit from noise? Surprisingly, wedemonstrate that under some circumstances, quantum noise can be used to improve the performanceof quantum reservoir computing, a prominent and recent quantum machine learning algorithm.Our results show that the amplitude damping noise can be beneficial to machine learning, whilethe depolarizing and phase damping noises should be prioritized for correction. This critical resultsheds new light into the physical mechanisms underlying quantum devices, providing solid practicalprescriptions for a successful implementation of quantum information processing in nowadayshardware.Fil: Domingo, L.. Instituto de Ciencias Matemáticas; España. Universidad Autónoma de Madrid; España. Universidad Politécnica de Madrid; EspañaFil: Carlo, Gabriel Gustavo. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Comisión Nacional de Energía Atómica; ArgentinaFil: Borondo, F.. Universidad Autónoma de Madrid; EspañaNature2023-04info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/232961Domingo, L.; Carlo, Gabriel Gustavo; Borondo, F.; Taking advantage of noise in quantum reservoir computing; Nature; Scientific Reports; 13; 1; 4-2023; 1-92045-2322CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1038/s41598-023-35461-5info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-03T09:45:43Zoai:ri.conicet.gov.ar:11336/232961instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-03 09:45:44.062CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Taking advantage of noise in quantum reservoir computing
title Taking advantage of noise in quantum reservoir computing
spellingShingle Taking advantage of noise in quantum reservoir computing
Domingo, L.
Reservoir Computing
Quantum Computation
Quantum Machine Learning
title_short Taking advantage of noise in quantum reservoir computing
title_full Taking advantage of noise in quantum reservoir computing
title_fullStr Taking advantage of noise in quantum reservoir computing
title_full_unstemmed Taking advantage of noise in quantum reservoir computing
title_sort Taking advantage of noise in quantum reservoir computing
dc.creator.none.fl_str_mv Domingo, L.
Carlo, Gabriel Gustavo
Borondo, F.
author Domingo, L.
author_facet Domingo, L.
Carlo, Gabriel Gustavo
Borondo, F.
author_role author
author2 Carlo, Gabriel Gustavo
Borondo, F.
author2_role author
author
dc.subject.none.fl_str_mv Reservoir Computing
Quantum Computation
Quantum Machine Learning
topic Reservoir Computing
Quantum Computation
Quantum Machine Learning
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.3
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv The biggest challenge that quantum computing and quantum machine learning are currently facingis the presence of noise in quantum devices. As a result, big efforts have been put into correctingor mitigating the induced errors. But, can these two fields benefit from noise? Surprisingly, wedemonstrate that under some circumstances, quantum noise can be used to improve the performanceof quantum reservoir computing, a prominent and recent quantum machine learning algorithm.Our results show that the amplitude damping noise can be beneficial to machine learning, whilethe depolarizing and phase damping noises should be prioritized for correction. This critical resultsheds new light into the physical mechanisms underlying quantum devices, providing solid practicalprescriptions for a successful implementation of quantum information processing in nowadayshardware.
Fil: Domingo, L.. Instituto de Ciencias Matemáticas; España. Universidad Autónoma de Madrid; España. Universidad Politécnica de Madrid; España
Fil: Carlo, Gabriel Gustavo. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Comisión Nacional de Energía Atómica; Argentina
Fil: Borondo, F.. Universidad Autónoma de Madrid; España
description The biggest challenge that quantum computing and quantum machine learning are currently facingis the presence of noise in quantum devices. As a result, big efforts have been put into correctingor mitigating the induced errors. But, can these two fields benefit from noise? Surprisingly, wedemonstrate that under some circumstances, quantum noise can be used to improve the performanceof quantum reservoir computing, a prominent and recent quantum machine learning algorithm.Our results show that the amplitude damping noise can be beneficial to machine learning, whilethe depolarizing and phase damping noises should be prioritized for correction. This critical resultsheds new light into the physical mechanisms underlying quantum devices, providing solid practicalprescriptions for a successful implementation of quantum information processing in nowadayshardware.
publishDate 2023
dc.date.none.fl_str_mv 2023-04
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/232961
Domingo, L.; Carlo, Gabriel Gustavo; Borondo, F.; Taking advantage of noise in quantum reservoir computing; Nature; Scientific Reports; 13; 1; 4-2023; 1-9
2045-2322
CONICET Digital
CONICET
url http://hdl.handle.net/11336/232961
identifier_str_mv Domingo, L.; Carlo, Gabriel Gustavo; Borondo, F.; Taking advantage of noise in quantum reservoir computing; Nature; Scientific Reports; 13; 1; 4-2023; 1-9
2045-2322
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1038/s41598-023-35461-5
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Nature
publisher.none.fl_str_mv Nature
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 13.13397

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