Exploring quantum localization with machine learning

Autores
Montes, Javier; Ermann, Leonardo; Rivas, Alejandro Mariano Fidel; Borondo, Florentino; Carlo, Gabriel Gustavo
Año de publicación
2024
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
We introduce an efficient neural network (NN) architecture for classifying wave functions in terms of their localization. Our approach integrates a versatile quantum phase space parametrization leading to a custom ”quantum” NN, with the pattern recognition capabilities of a modified convolutional model. This design accepts wave functions of any dimension as inputs and makes accurate predictions at an affordable computational cost. This scalability becomes crucial to explore the localization rate at the semiclassical limit, a long standing question in the quantum scattering field. Moreover, the physical meaning built in the model allows for the interpretation of the learning process.
Fil: Montes, Javier. Universidad Autónoma de Madrid; España
Fil: Ermann, Leonardo. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Comisión Nacional de Energía Atómica; Argentina
Fil: Rivas, Alejandro Mariano Fidel. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Comisión Nacional de Energía Atómica; Argentina
Fil: Borondo, Florentino. Universidad Autónoma de Madrid; España
Fil: Carlo, Gabriel Gustavo. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Comisión Nacional de Energía Atómica; Argentina
Materia
Neural network
Quantum Localization
Machine Learning
Quantum chaos
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:11336/239114
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/239114
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Exploring quantum localization with machine learningMontes, JavierErmann, LeonardoRivas, Alejandro Mariano FidelBorondo, FlorentinoCarlo, Gabriel GustavoNeural networkQuantum LocalizationMachine LearningQuantum chaoshttps://purl.org/becyt/ford/1.3https://purl.org/becyt/ford/1We introduce an efficient neural network (NN) architecture for classifying wave functions in terms of their localization. Our approach integrates a versatile quantum phase space parametrization leading to a custom ”quantum” NN, with the pattern recognition capabilities of a modified convolutional model. This design accepts wave functions of any dimension as inputs and makes accurate predictions at an affordable computational cost. This scalability becomes crucial to explore the localization rate at the semiclassical limit, a long standing question in the quantum scattering field. Moreover, the physical meaning built in the model allows for the interpretation of the learning process.Fil: Montes, Javier. Universidad Autónoma de Madrid; EspañaFil: Ermann, Leonardo. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Comisión Nacional de Energía Atómica; ArgentinaFil: Rivas, Alejandro Mariano Fidel. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Comisión Nacional de Energía Atómica; ArgentinaFil: Borondo, Florentino. Universidad Autónoma de Madrid; EspañaFil: Carlo, Gabriel Gustavo. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Comisión Nacional de Energía Atómica; ArgentinaCornell University2024-06info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/239114Montes, Javier; Ermann, Leonardo; Rivas, Alejandro Mariano Fidel; Borondo, Florentino; Carlo, Gabriel Gustavo; Exploring quantum localization with machine learning; Cornell University; Arxiv; 6-2024; 1-152331-8422CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://arxiv.org/abs/2406.00363info:eu-repo/semantics/altIdentifier/doi/10.48550/arXiv.2406.00363info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-10T13:17:47Zoai:ri.conicet.gov.ar:11336/239114instacron: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:34982025-09-10 13:17:48.23CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Exploring quantum localization with machine learning
title Exploring quantum localization with machine learning
spellingShingle Exploring quantum localization with machine learning
Montes, Javier
Neural network
Quantum Localization
Machine Learning
Quantum chaos
title_short Exploring quantum localization with machine learning
title_full Exploring quantum localization with machine learning
title_fullStr Exploring quantum localization with machine learning
title_full_unstemmed Exploring quantum localization with machine learning
title_sort Exploring quantum localization with machine learning
dc.creator.none.fl_str_mv Montes, Javier
Ermann, Leonardo
Rivas, Alejandro Mariano Fidel
Borondo, Florentino
Carlo, Gabriel Gustavo
author Montes, Javier
author_facet Montes, Javier
Ermann, Leonardo
Rivas, Alejandro Mariano Fidel
Borondo, Florentino
Carlo, Gabriel Gustavo
author_role author
author2 Ermann, Leonardo
Rivas, Alejandro Mariano Fidel
Borondo, Florentino
Carlo, Gabriel Gustavo
author2_role author
author
author
author
dc.subject.none.fl_str_mv Neural network
Quantum Localization
Machine Learning
Quantum chaos
topic Neural network
Quantum Localization
Machine Learning
Quantum chaos
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.3
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv We introduce an efficient neural network (NN) architecture for classifying wave functions in terms of their localization. Our approach integrates a versatile quantum phase space parametrization leading to a custom ”quantum” NN, with the pattern recognition capabilities of a modified convolutional model. This design accepts wave functions of any dimension as inputs and makes accurate predictions at an affordable computational cost. This scalability becomes crucial to explore the localization rate at the semiclassical limit, a long standing question in the quantum scattering field. Moreover, the physical meaning built in the model allows for the interpretation of the learning process.
Fil: Montes, Javier. Universidad Autónoma de Madrid; España
Fil: Ermann, Leonardo. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Comisión Nacional de Energía Atómica; Argentina
Fil: Rivas, Alejandro Mariano Fidel. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Comisión Nacional de Energía Atómica; Argentina
Fil: Borondo, Florentino. Universidad Autónoma de Madrid; España
Fil: Carlo, Gabriel Gustavo. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Comisión Nacional de Energía Atómica; Argentina
description We introduce an efficient neural network (NN) architecture for classifying wave functions in terms of their localization. Our approach integrates a versatile quantum phase space parametrization leading to a custom ”quantum” NN, with the pattern recognition capabilities of a modified convolutional model. This design accepts wave functions of any dimension as inputs and makes accurate predictions at an affordable computational cost. This scalability becomes crucial to explore the localization rate at the semiclassical limit, a long standing question in the quantum scattering field. Moreover, the physical meaning built in the model allows for the interpretation of the learning process.
publishDate 2024
dc.date.none.fl_str_mv 2024-06
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/11336/239114
Montes, Javier; Ermann, Leonardo; Rivas, Alejandro Mariano Fidel; Borondo, Florentino; Carlo, Gabriel Gustavo; Exploring quantum localization with machine learning; Cornell University; Arxiv; 6-2024; 1-15
2331-8422
CONICET Digital
CONICET
url http://hdl.handle.net/11336/239114
identifier_str_mv Montes, Javier; Ermann, Leonardo; Rivas, Alejandro Mariano Fidel; Borondo, Florentino; Carlo, Gabriel Gustavo; Exploring quantum localization with machine learning; Cornell University; Arxiv; 6-2024; 1-15
2331-8422
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://arxiv.org/abs/2406.00363
info:eu-repo/semantics/altIdentifier/doi/10.48550/arXiv.2406.00363
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
application/pdf
dc.publisher.none.fl_str_mv Cornell University
publisher.none.fl_str_mv Cornell University
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 12.993085

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