Quantum Bayesian Inference with renormalization for gravitational waves

Advancements in gravitational-wave (GW) interferometers, particularly the next generation, are poised to enable the detections of orders of magnitude more GWs from compact binary coalescences. While the surge in detections will profoundly advance GW astronomy and multimessenger astrophysics, it also...

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Detalles Bibliográficos
Autores: Escrig, Gabriel, Campos, Roberto, Qi, Hong, Martín-Delgado Alcántara, Miguel Ángel
Tipo de recurso: artículo
Fecha de publicación:2025
País:España
Institución:Universidad Complutense de Madrid (UCM)
Repositorio:Docta Complutense
Idioma:inglés
OAI Identifier:oai:docta.ucm.es:20.500.14352/122422
Acceso en línea:https://hdl.handle.net/20.500.14352/122422
Access Level:acceso abierto
Palabra clave:53
Gravitational waves
Algorithms
Markov chain Monte Carlo
Gravitational wave sources
Física (Física)
2212 Física Teórica
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oai_identifier_str oai:docta.ucm.es:20.500.14352/122422
network_acronym_str ES
network_name_str España
repository_id_str
spelling Quantum Bayesian Inference with renormalization for gravitational wavesEscrig, GabrielCampos, RobertoQi, HongMartín-Delgado Alcántara, Miguel Ángel53Gravitational wavesAlgorithmsMarkov chain Monte CarloGravitational wave sourcesFísica (Física)2212 Física TeóricaAdvancements in gravitational-wave (GW) interferometers, particularly the next generation, are poised to enable the detections of orders of magnitude more GWs from compact binary coalescences. While the surge in detections will profoundly advance GW astronomy and multimessenger astrophysics, it also poses significant computational challenges in parameter estimation. In this work, we introduce a hybrid quantum algorithm qBIRD, which performs quantum Bayesian inference with renormalization and downsampling to infer GW parameters. We validate the algorithm using both simulated and observed GWs from binary black hole mergers on quantum simulators, demonstrating that its accuracy is comparable to classical Markov Chain Monte Carlo methods. Currently, our analyses focus on a subset of parameters, including chirp mass and mass ratio, due to the limitations from classical hardware in simulating quantum algorithms. However, qBIRD can accommodate a broader parameter space when the constraints are eliminated with a small-scale quantum computer of sufficient logical qubits.IOP PublishingUniversidad Complutense de Madrid20252025-01-0120252025-01-01journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/20.500.14352/122422reponame:Docta Complutenseinstname:Universidad Complutense de Madrid (UCM)InglésengAgencia Estatal de Investigación http://dx.doi.org/10.13039/501100011033 Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023 PID2021-122547NB-I00 TECNOLOGIAS CLAVE PARA COMPUTACION CUANTICAopen accesshttp://purl.org/coar/access_right/c_abf2Attribution 4.0 Internationalhttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:docta.ucm.es:20.500.14352/1224222026-06-02T12:44:21Z
dc.title.none.fl_str_mv Quantum Bayesian Inference with renormalization for gravitational waves
title Quantum Bayesian Inference with renormalization for gravitational waves
spellingShingle Quantum Bayesian Inference with renormalization for gravitational waves
Escrig, Gabriel
53
Gravitational waves
Algorithms
Markov chain Monte Carlo
Gravitational wave sources
Física (Física)
2212 Física Teórica
title_short Quantum Bayesian Inference with renormalization for gravitational waves
title_full Quantum Bayesian Inference with renormalization for gravitational waves
title_fullStr Quantum Bayesian Inference with renormalization for gravitational waves
title_full_unstemmed Quantum Bayesian Inference with renormalization for gravitational waves
title_sort Quantum Bayesian Inference with renormalization for gravitational waves
dc.creator.none.fl_str_mv Escrig, Gabriel
Campos, Roberto
Qi, Hong
Martín-Delgado Alcántara, Miguel Ángel
author Escrig, Gabriel
author_facet Escrig, Gabriel
Campos, Roberto
Qi, Hong
Martín-Delgado Alcántara, Miguel Ángel
author_role author
author2 Campos, Roberto
Qi, Hong
Martín-Delgado Alcántara, Miguel Ángel
author2_role author
author
author
dc.contributor.none.fl_str_mv Universidad Complutense de Madrid
dc.subject.none.fl_str_mv 53
Gravitational waves
Algorithms
Markov chain Monte Carlo
Gravitational wave sources
Física (Física)
2212 Física Teórica
topic 53
Gravitational waves
Algorithms
Markov chain Monte Carlo
Gravitational wave sources
Física (Física)
2212 Física Teórica
description Advancements in gravitational-wave (GW) interferometers, particularly the next generation, are poised to enable the detections of orders of magnitude more GWs from compact binary coalescences. While the surge in detections will profoundly advance GW astronomy and multimessenger astrophysics, it also poses significant computational challenges in parameter estimation. In this work, we introduce a hybrid quantum algorithm qBIRD, which performs quantum Bayesian inference with renormalization and downsampling to infer GW parameters. We validate the algorithm using both simulated and observed GWs from binary black hole mergers on quantum simulators, demonstrating that its accuracy is comparable to classical Markov Chain Monte Carlo methods. Currently, our analyses focus on a subset of parameters, including chirp mass and mass ratio, due to the limitations from classical hardware in simulating quantum algorithms. However, qBIRD can accommodate a broader parameter space when the constraints are eliminated with a small-scale quantum computer of sufficient logical qubits.
publishDate 2025
dc.date.none.fl_str_mv 2025
2025-01-01
2025
2025-01-01
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/20.500.14352/122422
url https://hdl.handle.net/20.500.14352/122422
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.relation.none.fl_str_mv Agencia Estatal de Investigación http://dx.doi.org/10.13039/501100011033 Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023 PID2021-122547NB-I00 TECNOLOGIAS CLAVE PARA COMPUTACION CUANTICA
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:Docta Complutense
instname:Universidad Complutense de Madrid (UCM)
instname_str Universidad Complutense de Madrid (UCM)
reponame_str Docta Complutense
collection Docta Complutense
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,812429

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