Parameter estimation from quantum-jump data using neural networks

We present an inference method utilizing artificial neural networks for parameter estimation of a quantum probe monitored through a single continuous measurement. Unlike existing approaches focusing on the diffusive signals generated by continuous weak measurements, our method harnesses quantum corr...

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Detalles Bibliográficos
Autores: Rinaldi, Enrico, Ahmed, Shahnawaz, Khanahmadi, Maryam, Nori, Franco, González Lastre, Manuel Eduardo, García Herreros, Sergio, Sánchez Muñoz, Carlos
Tipo de recurso: artículo
Fecha de publicación:2024
País:España
Institución:Universidad Autónoma de Madrid
Repositorio:Biblos-e Archivo. Repositorio Institucional de la UAM
Idioma:inglés
OAI Identifier:oai:repositorio.uam.es:10486/717142
Acceso en línea:http://hdl.handle.net/10486/717142
https://dx.doi.org/10.1088/2058-9565/ad3c68
Access Level:acceso abierto
Palabra clave:deep learning
neural networks
photon counting
quantum jumps
quantum metrology
quantum parameter estimation
Física
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spelling Parameter estimation from quantum-jump data using neural networksRinaldi, EnricoAhmed, ShahnawazKhanahmadi, MaryamNori, FrancoGonzález Lastre, Manuel EduardoGarcía Herreros, SergioSánchez Muñoz, Carlosdeep learningneural networksphoton countingquantum jumpsquantum metrologyquantum parameter estimationFísicaWe present an inference method utilizing artificial neural networks for parameter estimation of a quantum probe monitored through a single continuous measurement. Unlike existing approaches focusing on the diffusive signals generated by continuous weak measurements, our method harnesses quantum correlations in discrete photon-counting data characterized by quantum jumps. We benchmark the precision of this method against Bayesian inference, which is optimal in the sense of information retrieval. By using numerical experiments on a two-level quantum system, we demonstrate that our approach can achieve a similar optimal performance as Bayesian inference, while drastically reducing computational costs. Additionally, the method exhibits robustness against the presence of imperfections in both measurement and training data. This approach offers a promising and computationally efficient tool for quantum parameter estimation with photon-counting data, relevant for applications such as quantum sensing or quantum imaging, as well as robust calibration tasks in laboratory-based settingsAuthors thank Yexiong Zeng, Andrey Kardashin, Clemens Gneiting, Yanming Che, Fabrizio Minganti, Anton Frisk Kockum, Yuta Kikuchi, and Marcello Benedetti for critical reading and feedback. E R was supported by Nippon Telegraph and Telephone Corporation (NTT) Research during the early stages of this work. S A and M K acknowledge support from the Knut and Alice Wallenberg Foundation through the Wallenberg Centre for Quantum Technology (WACQT). M G L acknowledges financial support from the Spanish Ministerio de Ciencia e Innovaci\u00F3n, through project PID2020-115864RB-I00 and grant PRE2021-098697. F N is supported in part by Nippon Telegraph and Telephone Corporation (NTT) Research, the Japan Science and Technology Agency (JST) [via the Quantum Leap Flagship Program (Q-LEAP) and the Moonshot R&D Grant No. JPMJMS2061], the Asian Office of Aerospace Research and Development (AOARD) (via Grant No. FA2386-20-1-4069), and the Office of Naval Research Global (ONR) (via GrantNo. N62909-23-1-2074). C S M acknowledges that the project that gave rise to these results received the support of a fellowship from \u2018la Caixa\u2019 Foundation (ID 100010434) and from the European Union\u2019s Horizon 2020 research and innovation programme under the Marie Sk\u0142odowska-Curie Grant Agreement No.847648, with fellowship code LCF/BQ/PI20/11760026, and financial support from the MCINN projects PID2021-126964OB-I00 (QENIGMA) and the Proyecto Sin\u00E9rgico CAM 2020 Y2020/TCS- 6545 (NanoQuCo-CM). We acknowledge the usage of the HOKUSAI BigWaterfall (HBW) cluster from the Information System Division of RIKEN.IOP PublishingDepartamento de Física Teórica de la Materia CondensadaFacultad de Ciencias20242024-07-01research articlehttp://purl.org/coar/resource_type/c_2df8fbb1AMhttp://purl.org/coar/version/c_ab4af688f83e57aainfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10486/717142https://dx.doi.org/10.1088/2058-9565/ad3c68reponame:Biblos-e Archivo. Repositorio Institucional de la UAMinstname:Universidad Autónoma de MadridInglésengopen accesshttp://purl.org/coar/access_right/c_abf2Attribution 4.0 Internationalhttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:repositorio.uam.es:10486/7171422026-06-23T12:46:27Z
dc.title.none.fl_str_mv Parameter estimation from quantum-jump data using neural networks
title Parameter estimation from quantum-jump data using neural networks
spellingShingle Parameter estimation from quantum-jump data using neural networks
Rinaldi, Enrico
deep learning
neural networks
photon counting
quantum jumps
quantum metrology
quantum parameter estimation
Física
title_short Parameter estimation from quantum-jump data using neural networks
title_full Parameter estimation from quantum-jump data using neural networks
title_fullStr Parameter estimation from quantum-jump data using neural networks
title_full_unstemmed Parameter estimation from quantum-jump data using neural networks
title_sort Parameter estimation from quantum-jump data using neural networks
dc.creator.none.fl_str_mv Rinaldi, Enrico
Ahmed, Shahnawaz
Khanahmadi, Maryam
Nori, Franco
González Lastre, Manuel Eduardo
García Herreros, Sergio
Sánchez Muñoz, Carlos
author Rinaldi, Enrico
author_facet Rinaldi, Enrico
Ahmed, Shahnawaz
Khanahmadi, Maryam
Nori, Franco
González Lastre, Manuel Eduardo
García Herreros, Sergio
Sánchez Muñoz, Carlos
author_role author
author2 Ahmed, Shahnawaz
Khanahmadi, Maryam
Nori, Franco
González Lastre, Manuel Eduardo
García Herreros, Sergio
Sánchez Muñoz, Carlos
author2_role author
author
author
author
author
author
dc.contributor.none.fl_str_mv Departamento de Física Teórica de la Materia Condensada
Facultad de Ciencias
dc.subject.none.fl_str_mv deep learning
neural networks
photon counting
quantum jumps
quantum metrology
quantum parameter estimation
Física
topic deep learning
neural networks
photon counting
quantum jumps
quantum metrology
quantum parameter estimation
Física
description We present an inference method utilizing artificial neural networks for parameter estimation of a quantum probe monitored through a single continuous measurement. Unlike existing approaches focusing on the diffusive signals generated by continuous weak measurements, our method harnesses quantum correlations in discrete photon-counting data characterized by quantum jumps. We benchmark the precision of this method against Bayesian inference, which is optimal in the sense of information retrieval. By using numerical experiments on a two-level quantum system, we demonstrate that our approach can achieve a similar optimal performance as Bayesian inference, while drastically reducing computational costs. Additionally, the method exhibits robustness against the presence of imperfections in both measurement and training data. This approach offers a promising and computationally efficient tool for quantum parameter estimation with photon-counting data, relevant for applications such as quantum sensing or quantum imaging, as well as robust calibration tasks in laboratory-based settings
publishDate 2024
dc.date.none.fl_str_mv 2024
2024-07-01
dc.type.none.fl_str_mv research article
http://purl.org/coar/resource_type/c_2df8fbb1
AM
http://purl.org/coar/version/c_ab4af688f83e57aa
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv http://hdl.handle.net/10486/717142
https://dx.doi.org/10.1088/2058-9565/ad3c68
url http://hdl.handle.net/10486/717142
https://dx.doi.org/10.1088/2058-9565/ad3c68
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution 4.0 International
http://creativecommons.org/licenses/by/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution 4.0 International
http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv IOP Publishing
publisher.none.fl_str_mv IOP Publishing
dc.source.none.fl_str_mv reponame:Biblos-e Archivo. Repositorio Institucional de la UAM
instname:Universidad Autónoma de Madrid
instname_str Universidad Autónoma de Madrid
reponame_str Biblos-e Archivo. Repositorio Institucional de la UAM
collection Biblos-e Archivo. Repositorio Institucional de la UAM
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15.811543

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