Multiobjective variational quantum optimization for constrained problems: an application to cash handling

21 pags., 11 figs.

Detalles Bibliográficos
Autores: Díez-Valle, Pablo, Luis-Hita, Jorge, Hernández-Santana, Senaida, Martínez-García, Fernando, Díaz-Fernández, Álvaro, Andrés, Eva, García-Ripoll, Juan José, Sánchez-Martínez, Escolástico, Porras, Diego
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2023
País:España
Institución:Consejo Superior de Investigaciones Científicas (CSIC)
Repositorio:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/348886
Acceso en línea:http://hdl.handle.net/10261/348886
https://api.elsevier.com/content/abstract/scopus_id/85166217279
Access Level:acceso abierto
Palabra clave:Quantum computing
Quantum optimization
Variational quantum algorithms
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oai_identifier_str oai:digital.csic.es:10261/348886
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dc.title.none.fl_str_mv Multiobjective variational quantum optimization for constrained problems: an application to cash handling
title Multiobjective variational quantum optimization for constrained problems: an application to cash handling
spellingShingle Multiobjective variational quantum optimization for constrained problems: an application to cash handling
Díez-Valle, Pablo
Quantum computing
Quantum optimization
Variational quantum algorithms
title_short Multiobjective variational quantum optimization for constrained problems: an application to cash handling
title_full Multiobjective variational quantum optimization for constrained problems: an application to cash handling
title_fullStr Multiobjective variational quantum optimization for constrained problems: an application to cash handling
title_full_unstemmed Multiobjective variational quantum optimization for constrained problems: an application to cash handling
title_sort Multiobjective variational quantum optimization for constrained problems: an application to cash handling
dc.creator.none.fl_str_mv Díez-Valle, Pablo
Luis-Hita, Jorge
Hernández-Santana, Senaida
Martínez-García, Fernando
Díaz-Fernández, Álvaro
Andrés, Eva
García-Ripoll, Juan José
Sánchez-Martínez, Escolástico
Porras, Diego
author Díez-Valle, Pablo
author_facet Díez-Valle, Pablo
Luis-Hita, Jorge
Hernández-Santana, Senaida
Martínez-García, Fernando
Díaz-Fernández, Álvaro
Andrés, Eva
García-Ripoll, Juan José
Sánchez-Martínez, Escolástico
Porras, Diego
author_role author
author2 Luis-Hita, Jorge
Hernández-Santana, Senaida
Martínez-García, Fernando
Díaz-Fernández, Álvaro
Andrés, Eva
García-Ripoll, Juan José
Sánchez-Martínez, Escolástico
Porras, Diego
author2_role author
author
author
author
author
author
author
author
dc.contributor.none.fl_str_mv Ministerio de Ciencia e Innovación (España)
Consejo Superior de Investigaciones Científicas (España)
Comunidad de Madrid
CSIC - Secretaría General Adjunta de Informática (SGAI)
Centro de Supercomputación de Galicia
Díez-Valle, Pablo [0000-0001-8338-7973]
Luis-Hita, Jorge [0009-0005-4332-3170]
Hernández-Santana, Senaida [0000-0001-7779-0563]
Martínez-García, Fernando [0000-0001-7243-3663]
Díaz-Fernández, Álvaro [0000-0001-9432-7845]
Andrés, Eva [0000-0002-9451-340X]
García-Ripoll, Juan José [0000-0001-8993-4624]
Sánchez-Martínez, Escolástico [0000-0001-6652-290X]
Porras, Diego [0000-0003-2995-0299]
Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]
dc.subject.none.fl_str_mv Quantum computing
Quantum optimization
Variational quantum algorithms
topic Quantum computing
Quantum optimization
Variational quantum algorithms
description 21 pags., 11 figs.
publishDate 2023
dc.date.none.fl_str_mv 2023
2024
2024
dc.type.none.fl_str_mv info:eu-repo/semantics/article
http://purl.org/coar/resource_type/c_6501
Publisher's version
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10261/348886
https://api.elsevier.com/content/abstract/scopus_id/85166217279
url http://hdl.handle.net/10261/348886
https://api.elsevier.com/content/abstract/scopus_id/85166217279
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
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info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/PID2021-127968NB-I00
S2018/TCS-4342/QUITEMAD-CM
Quantum Science and Technology
https://doi.org/10.1088/2058-9565/ace474
Sí
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv IOP Publishing
publisher.none.fl_str_mv IOP Publishing
dc.source.none.fl_str_mv reponame:DIGITAL.CSIC. Repositorio Institucional del CSIC
instname:Consejo Superior de Investigaciones Científicas (CSIC)
instname_str Consejo Superior de Investigaciones Científicas (CSIC)
reponame_str DIGITAL.CSIC. Repositorio Institucional del CSIC
collection DIGITAL.CSIC. Repositorio Institucional del CSIC
repository.name.fl_str_mv
repository.mail.fl_str_mv
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spelling Multiobjective variational quantum optimization for constrained problems: an application to cash handlingDíez-Valle, PabloLuis-Hita, JorgeHernández-Santana, SenaidaMartínez-García, FernandoDíaz-Fernández, ÁlvaroAndrés, EvaGarcía-Ripoll, Juan JoséSánchez-Martínez, EscolásticoPorras, DiegoQuantum computingQuantum optimizationVariational quantum algorithms21 pags., 11 figs.Combinatorial optimization problems are ubiquitous in industry. In addition to finding a solution with minimum cost, problems of high relevance involve a number of constraints that the solution must satisfy. Variational quantum algorithms (VQAs) have emerged as promising candidates for solving these problems in the noisy intermediate-scale quantum stage. However, the constraints are often complex enough to make their efficient mapping to quantum hardware difficult or even infeasible. An alternative standard approach is to transform the optimization problem to include these constraints as penalty terms, but this method involves additional hyperparameters and does not ensure that the constraints are satisfied due to the existence of local minima. In this paper, we introduce a new method for solving combinatorial optimization problems with challenging constraints using VQAs. We propose the multi-objective variational constrained optimizer (MOVCO) to classically update the variational parameters by a multiobjective optimization performed by a genetic algorithm. This optimization allows the algorithm to progressively sample only states within the in-constraints space, while optimizing the energy of these states. We test our proposal on a real-world problem with great relevance in finance: the cash handling problem. We introduce a novel mathematical formulation for this problem, and compare the performance of MOVCO versus a penalty based optimization. Our empirical results show a significant improvement in terms of the cost of the achieved solutions, but especially in the avoidance of local minima that do not satisfy any of the mandatory constraints.This work was supported by the Spanish CDTI through Misiones Ciencia e Innovación Program (CUCO) under Grant MIG-20211005, PID2021-127968NB-I00 funded by MCIN/AEI/10.13039/501100011033/ FEDER,UE, and CSIC Interdisciplinary Thematic Platform (PTI) Quantum Technologies (PTI-QTEP). P D-V also acknowledges support from CAM/FEDER Project No. S2018/TCS-4342 (QUITEMAD-CM). The authors also gratefully acknowledge the Scientific computing Area (AIC), SGAI-CSIC, for their assistance while using the DRAGO Supercomputer for performing the simulations, and Centro de Supercomputación de Galicia (CESGA) who provided access to the supercomputer FinisTerrae.Peer reviewedIOP PublishingMinisterio de Ciencia e Innovación (España)Consejo Superior de Investigaciones Científicas (España)Comunidad de MadridCSIC - Secretaría General Adjunta de Informática (SGAI)Centro de Supercomputación de GaliciaDíez-Valle, Pablo [0000-0001-8338-7973]Luis-Hita, Jorge [0009-0005-4332-3170]Hernández-Santana, Senaida [0000-0001-7779-0563]Martínez-García, Fernando [0000-0001-7243-3663]Díaz-Fernández, Álvaro [0000-0001-9432-7845]Andrés, Eva [0000-0002-9451-340X]García-Ripoll, Juan José [0000-0001-8993-4624]Sánchez-Martínez, Escolástico [0000-0001-6652-290X]Porras, Diego [0000-0003-2995-0299]Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]202420242023info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501Publisher's versioninfo:eu-repo/semantics/publishedVersionhttp://hdl.handle.net/10261/348886https://api.elsevier.com/content/abstract/scopus_id/85166217279reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)Inglés#PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE#info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/PID2021-127968NB-I00S2018/TCS-4342/QUITEMAD-CMQuantum Science and Technologyhttps://doi.org/10.1088/2058-9565/ace474Síinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/3488862026-05-22T06:33:51Z
score 15,812429

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