Predicting the minimum control time of quantum protocols with artificial neural networks

Autores
Sevitz, Sofia; Mirkin, Nicolás; Wisniacki, Diego Ariel
Año de publicación
2023
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Quantum control relies on the driving of quantum states without the loss of coherence, thus the leakage of quantum properties into the environment over time is a fundamental challenge. One work-around is to implement fast protocols, hence the Minimal Control Time (MCT) is of upmost importance. Here, we employ a machine learning network in order to estimate the MCT in a state transfer protocol. An unsupervised learning approach is considered by using a combination of an autoencoder network with the k-means clustering tool. The Landau–Zener (LZ) Hamiltonian is analyzed given that it has an analytical MCT and a distinctive topology change in the control landscape when the total evolution time is either under or over the MCT. We obtain that the network is able to not only produce an estimation of the MCT but also gains an understanding of the landscape’s topologies. Similar results are found for the generalized LZ Hamiltonian while limitations to our very simple architecture were encountered.
Fil: Sevitz, Sofia. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Física; Argentina
Fil: Mirkin, Nicolás. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Física; Argentina
Fil: Wisniacki, Diego Ariel. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Física; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Física de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Física de Buenos Aires; Argentina
Materia
Control cuantico
Inteligencia artificial
tiempo minimo
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/254914
Acceder
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network_name_str CONICET Digital (CONICET)
spelling Predicting the minimum control time of quantum protocols with artificial neural networksSevitz, SofiaMirkin, NicolásWisniacki, Diego ArielControl cuanticoInteligencia artificialtiempo minimohttps://purl.org/becyt/ford/1.3https://purl.org/becyt/ford/1Quantum control relies on the driving of quantum states without the loss of coherence, thus the leakage of quantum properties into the environment over time is a fundamental challenge. One work-around is to implement fast protocols, hence the Minimal Control Time (MCT) is of upmost importance. Here, we employ a machine learning network in order to estimate the MCT in a state transfer protocol. An unsupervised learning approach is considered by using a combination of an autoencoder network with the k-means clustering tool. The Landau–Zener (LZ) Hamiltonian is analyzed given that it has an analytical MCT and a distinctive topology change in the control landscape when the total evolution time is either under or over the MCT. We obtain that the network is able to not only produce an estimation of the MCT but also gains an understanding of the landscape’s topologies. Similar results are found for the generalized LZ Hamiltonian while limitations to our very simple architecture were encountered.Fil: Sevitz, Sofia. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Física; ArgentinaFil: Mirkin, Nicolás. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Física; ArgentinaFil: Wisniacki, Diego Ariel. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Física; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Física de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Física de Buenos Aires; ArgentinaIOP Publishing2023-06info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/254914Sevitz, Sofia; Mirkin, Nicolás; Wisniacki, Diego Ariel; Predicting the minimum control time of quantum protocols with artificial neural networks; IOP Publishing; Quantum Science and Technology; 8; 3; 6-2023; 1-142058-9565CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://iopscience.iop.org/article/10.1088/2058-9565/acd579info:eu-repo/semantics/altIdentifier/doi/10.1088/2058-9565/acd579info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-10-22T11:08:25Zoai:ri.conicet.gov.ar:11336/254914instacron: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:34982025-10-22 11:08:25.4CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Predicting the minimum control time of quantum protocols with artificial neural networks
title Predicting the minimum control time of quantum protocols with artificial neural networks
spellingShingle Predicting the minimum control time of quantum protocols with artificial neural networks
Sevitz, Sofia
Control cuantico
Inteligencia artificial
tiempo minimo
title_short Predicting the minimum control time of quantum protocols with artificial neural networks
title_full Predicting the minimum control time of quantum protocols with artificial neural networks
title_fullStr Predicting the minimum control time of quantum protocols with artificial neural networks
title_full_unstemmed Predicting the minimum control time of quantum protocols with artificial neural networks
title_sort Predicting the minimum control time of quantum protocols with artificial neural networks
dc.creator.none.fl_str_mv Sevitz, Sofia
Mirkin, Nicolás
Wisniacki, Diego Ariel
author Sevitz, Sofia
author_facet Sevitz, Sofia
Mirkin, Nicolás
Wisniacki, Diego Ariel
author_role author
author2 Mirkin, Nicolás
Wisniacki, Diego Ariel
author2_role author
author
dc.subject.none.fl_str_mv Control cuantico
Inteligencia artificial
tiempo minimo
topic Control cuantico
Inteligencia artificial
tiempo minimo
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.3
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv Quantum control relies on the driving of quantum states without the loss of coherence, thus the leakage of quantum properties into the environment over time is a fundamental challenge. One work-around is to implement fast protocols, hence the Minimal Control Time (MCT) is of upmost importance. Here, we employ a machine learning network in order to estimate the MCT in a state transfer protocol. An unsupervised learning approach is considered by using a combination of an autoencoder network with the k-means clustering tool. The Landau–Zener (LZ) Hamiltonian is analyzed given that it has an analytical MCT and a distinctive topology change in the control landscape when the total evolution time is either under or over the MCT. We obtain that the network is able to not only produce an estimation of the MCT but also gains an understanding of the landscape’s topologies. Similar results are found for the generalized LZ Hamiltonian while limitations to our very simple architecture were encountered.
Fil: Sevitz, Sofia. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Física; Argentina
Fil: Mirkin, Nicolás. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Física; Argentina
Fil: Wisniacki, Diego Ariel. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Física; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Física de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Física de Buenos Aires; Argentina
description Quantum control relies on the driving of quantum states without the loss of coherence, thus the leakage of quantum properties into the environment over time is a fundamental challenge. One work-around is to implement fast protocols, hence the Minimal Control Time (MCT) is of upmost importance. Here, we employ a machine learning network in order to estimate the MCT in a state transfer protocol. An unsupervised learning approach is considered by using a combination of an autoencoder network with the k-means clustering tool. The Landau–Zener (LZ) Hamiltonian is analyzed given that it has an analytical MCT and a distinctive topology change in the control landscape when the total evolution time is either under or over the MCT. We obtain that the network is able to not only produce an estimation of the MCT but also gains an understanding of the landscape’s topologies. Similar results are found for the generalized LZ Hamiltonian while limitations to our very simple architecture were encountered.
publishDate 2023
dc.date.none.fl_str_mv 2023-06
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/11336/254914
Sevitz, Sofia; Mirkin, Nicolás; Wisniacki, Diego Ariel; Predicting the minimum control time of quantum protocols with artificial neural networks; IOP Publishing; Quantum Science and Technology; 8; 3; 6-2023; 1-14
2058-9565
CONICET Digital
CONICET
url http://hdl.handle.net/11336/254914
identifier_str_mv Sevitz, Sofia; Mirkin, Nicolás; Wisniacki, Diego Ariel; Predicting the minimum control time of quantum protocols with artificial neural networks; IOP Publishing; Quantum Science and Technology; 8; 3; 6-2023; 1-14
2058-9565
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://iopscience.iop.org/article/10.1088/2058-9565/acd579
info:eu-repo/semantics/altIdentifier/doi/10.1088/2058-9565/acd579
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
application/pdf
dc.publisher.none.fl_str_mv IOP Publishing
publisher.none.fl_str_mv IOP Publishing
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 13.229304

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