Automatic recalibration of quantum devices by reinforcing learning

Autores
Crosta, Tomás; Rebón, Lorena; Vilariño, Fernando; Matera, Juan Mauricio; Bilkis, Matías
Año de publicación
2025
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
During their operation, due to shifts in environmental conditions, devices undergo various forms of detuning from their optimal settings. Typically, this is addressed through control loops, which monitor variables and the device performance, to maintain settings at their optimal values. Quantum devices are particularly challenging since their functionality relies on precisely tuning their parameters. At the same time, the detailed modeling of the environmental behavior is often computationally unaffordable, while a direct measure of the parameters defining the system state is costly and introduces extra noise in the mechanism. In this study, we investigate the application of reinforcement learning techniques to develop a model-free control loop for continuous recalibration of quantum device parameters. Furthermore, we explore the advantages of incorporating minimal environmental noise models. As an example, the application to numerical simulations of a Kennedy receiver-based long-distance quantum communication protocol is presented.
Fil: Crosta, Tomás. Computer Vision Center (CVC). Barcelona. España
Fil: Rebón, Lorena. Universidad Nacional de La Plata - CONICET. Instituto de Física La Plata (IFLP); Argentina
Fil: Vilariño, Fernando. Computer Vision Center (CVC); España
Fil: Matera, Juan Mauricio. Universidad Nacional de La Plata - CONICET. Instituto de Física La Plata (IFLP); Argentina
Fil: Bilkis, Matías. Computer Vision Center (CVC); España
Fuente
An. (Asoc. Fís. Argent., En línea) 2025;04(36):95-105
Materia
QUANTUM MACHINE LEARNING
QUANTUM CONTROL
AUTOMATIC RE-CALIBRATION
KENEDY RECEIVER
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar
Repositorio
Biblioteca Digital (UBA-FCEN)
Institución
Universidad Nacional de Buenos Aires. Facultad de Ciencias Exactas y Naturales
OAI Identificador
afa:afa_v36_n04_p095
Acceder
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network_name_str Biblioteca Digital (UBA-FCEN)
spelling Automatic recalibration of quantum devices by reinforcing learningCrosta, TomásRebón, LorenaVilariño, FernandoMatera, Juan MauricioBilkis, MatíasQUANTUM MACHINE LEARNINGQUANTUM CONTROLAUTOMATIC RE-CALIBRATIONKENEDY RECEIVERDuring their operation, due to shifts in environmental conditions, devices undergo various forms of detuning from their optimal settings. Typically, this is addressed through control loops, which monitor variables and the device performance, to maintain settings at their optimal values. Quantum devices are particularly challenging since their functionality relies on precisely tuning their parameters. At the same time, the detailed modeling of the environmental behavior is often computationally unaffordable, while a direct measure of the parameters defining the system state is costly and introduces extra noise in the mechanism. In this study, we investigate the application of reinforcement learning techniques to develop a model-free control loop for continuous recalibration of quantum device parameters. Furthermore, we explore the advantages of incorporating minimal environmental noise models. As an example, the application to numerical simulations of a Kennedy receiver-based long-distance quantum communication protocol is presented.Fil: Crosta, Tomás. Computer Vision Center (CVC). Barcelona. EspañaFil: Rebón, Lorena. Universidad Nacional de La Plata - CONICET. Instituto de Física La Plata (IFLP); ArgentinaFil: Vilariño, Fernando. Computer Vision Center (CVC); EspañaFil: Matera, Juan Mauricio. Universidad Nacional de La Plata - CONICET. Instituto de Física La Plata (IFLP); ArgentinaFil: Bilkis, Matías. Computer Vision Center (CVC); EspañaAsociación Física Argentina2025info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfhttps://hdl.handle.net/20.500.12110/afa_v36_n04_p095An. (Asoc. Fís. Argent., En línea) 2025;04(36):95-105reponame:Biblioteca Digital (UBA-FCEN)instname:Universidad Nacional de Buenos Aires. Facultad de Ciencias Exactas y Naturalesinstacron:UBA-FCENenginfo:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar2026-04-16T09:45:15Zafa:afa_v36_n04_p095Institucionalhttps://digital.bl.fcen.uba.ar/Universidad públicaNo correspondehttps://digital.bl.fcen.uba.ar/cgi-bin/oaiserver.cgiana@bl.fcen.uba.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:18962026-04-16 09:45:18.553Biblioteca Digital (UBA-FCEN) - Universidad Nacional de Buenos Aires. Facultad de Ciencias Exactas y Naturalesfalse
dc.title.none.fl_str_mv Automatic recalibration of quantum devices by reinforcing learning
title Automatic recalibration of quantum devices by reinforcing learning
spellingShingle Automatic recalibration of quantum devices by reinforcing learning
Crosta, Tomás
QUANTUM MACHINE LEARNING
QUANTUM CONTROL
AUTOMATIC RE-CALIBRATION
KENEDY RECEIVER
title_short Automatic recalibration of quantum devices by reinforcing learning
title_full Automatic recalibration of quantum devices by reinforcing learning
title_fullStr Automatic recalibration of quantum devices by reinforcing learning
title_full_unstemmed Automatic recalibration of quantum devices by reinforcing learning
title_sort Automatic recalibration of quantum devices by reinforcing learning
dc.creator.none.fl_str_mv Crosta, Tomás
Rebón, Lorena
Vilariño, Fernando
Matera, Juan Mauricio
Bilkis, Matías
author Crosta, Tomás
author_facet Crosta, Tomás
Rebón, Lorena
Vilariño, Fernando
Matera, Juan Mauricio
Bilkis, Matías
author_role author
author2 Rebón, Lorena
Vilariño, Fernando
Matera, Juan Mauricio
Bilkis, Matías
author2_role author
author
author
author
dc.subject.none.fl_str_mv QUANTUM MACHINE LEARNING
QUANTUM CONTROL
AUTOMATIC RE-CALIBRATION
KENEDY RECEIVER
topic QUANTUM MACHINE LEARNING
QUANTUM CONTROL
AUTOMATIC RE-CALIBRATION
KENEDY RECEIVER
dc.description.none.fl_txt_mv During their operation, due to shifts in environmental conditions, devices undergo various forms of detuning from their optimal settings. Typically, this is addressed through control loops, which monitor variables and the device performance, to maintain settings at their optimal values. Quantum devices are particularly challenging since their functionality relies on precisely tuning their parameters. At the same time, the detailed modeling of the environmental behavior is often computationally unaffordable, while a direct measure of the parameters defining the system state is costly and introduces extra noise in the mechanism. In this study, we investigate the application of reinforcement learning techniques to develop a model-free control loop for continuous recalibration of quantum device parameters. Furthermore, we explore the advantages of incorporating minimal environmental noise models. As an example, the application to numerical simulations of a Kennedy receiver-based long-distance quantum communication protocol is presented.
Fil: Crosta, Tomás. Computer Vision Center (CVC). Barcelona. España
Fil: Rebón, Lorena. Universidad Nacional de La Plata - CONICET. Instituto de Física La Plata (IFLP); Argentina
Fil: Vilariño, Fernando. Computer Vision Center (CVC); España
Fil: Matera, Juan Mauricio. Universidad Nacional de La Plata - CONICET. Instituto de Física La Plata (IFLP); Argentina
Fil: Bilkis, Matías. Computer Vision Center (CVC); España
description During their operation, due to shifts in environmental conditions, devices undergo various forms of detuning from their optimal settings. Typically, this is addressed through control loops, which monitor variables and the device performance, to maintain settings at their optimal values. Quantum devices are particularly challenging since their functionality relies on precisely tuning their parameters. At the same time, the detailed modeling of the environmental behavior is often computationally unaffordable, while a direct measure of the parameters defining the system state is costly and introduces extra noise in the mechanism. In this study, we investigate the application of reinforcement learning techniques to develop a model-free control loop for continuous recalibration of quantum device parameters. Furthermore, we explore the advantages of incorporating minimal environmental noise models. As an example, the application to numerical simulations of a Kennedy receiver-based long-distance quantum communication protocol is presented.
publishDate 2025
dc.date.none.fl_str_mv 2025
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 https://hdl.handle.net/20.500.12110/afa_v36_n04_p095
url https://hdl.handle.net/20.500.12110/afa_v36_n04_p095
dc.language.none.fl_str_mv eng
language eng
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Asociación Física Argentina
publisher.none.fl_str_mv Asociación Física Argentina
dc.source.none.fl_str_mv An. (Asoc. Fís. Argent., En línea) 2025;04(36):95-105
reponame:Biblioteca Digital (UBA-FCEN)
instname:Universidad Nacional de Buenos Aires. Facultad de Ciencias Exactas y Naturales
instacron:UBA-FCEN
reponame_str Biblioteca Digital (UBA-FCEN)
collection Biblioteca Digital (UBA-FCEN)
instname_str Universidad Nacional de Buenos Aires. Facultad de Ciencias Exactas y Naturales
instacron_str UBA-FCEN
institution UBA-FCEN
repository.name.fl_str_mv Biblioteca Digital (UBA-FCEN) - Universidad Nacional de Buenos Aires. Facultad de Ciencias Exactas y Naturales
repository.mail.fl_str_mv ana@bl.fcen.uba.ar
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score 12.692636

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