Automatic design of quantum feature maps

We propose a new technique for the automatic generation of optimal ad-hoc anstze for classification by using quantum support vector machine. This efficient method is based on non-sorted genetic algorithm II multiobjective genetic algorithms which allow both maximize the accuracy and minimize the ans...

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Autores: Altares-López, Sergio, Ribeiro Seijas, Ángela, García-Ripoll, Juan José
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2021
País:España
Institución:Consejo Superior de Investigaciones Científicas (CSIC)
Repositorio:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/261972
Acceso en línea:http://hdl.handle.net/10261/261972
https://api.elsevier.com/content/abstract/scopus_id/85115203955
Access Level:acceso abierto
Palabra clave:Artificial intelligence
Automatic quantum classifier generation
Genetic algorithms
Optimization
Quantum computing
Quantum machine learning
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spelling Automatic design of quantum feature mapsAltares-López, SergioRibeiro Seijas, ÁngelaGarcía-Ripoll, Juan JoséArtificial intelligenceAutomatic quantum classifier generationGenetic algorithmsOptimizationQuantum computingQuantum machine learningWe propose a new technique for the automatic generation of optimal ad-hoc anstze for classification by using quantum support vector machine. This efficient method is based on non-sorted genetic algorithm II multiobjective genetic algorithms which allow both maximize the accuracy and minimize the ansatz size. It is demonstrated the validity of the technique by a practical example with a non-linear dataset, interpreting the resulting circuit and its outputs. We also show other application fields of the technique that reinforce the validity of the method, and a comparison with classical classifiers in order to understand the advantages of using quantum machine learning.The authors gratefully acknowledges the computer resources at Artemisa, funded by the European Union ERDF and Comunitat Valenciana as well as the technical support provided by the Instituto de Física Corpuscular, IFIC (CSIC-UV). This work has been supported by Spanish Project PGC2018-094792-B-100 (MCIU/AEI/FEDER, EU), CAM/FEDER Project No. S2018/TCS-4342 (QUITEMAD-CM), and CSIC Platform PTI-001.Peer reviewedIOP PublishingAgencia Estatal de Investigación (España)Comunidad de MadridAltares-López, Sergio [0000-0002-0847-6113]Ribeiro Seijas, Ángela [0000-0001-5807-8132]García-Ripoll, Juan José [0000-0001-8993-4624]Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]202220222021info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501Publisher's versioninfo:eu-repo/semantics/publishedVersionhttp://hdl.handle.net/10261/261972https://api.elsevier.com/content/abstract/scopus_id/85115203955reponame: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 2017-2020/PGC2018-094792-B-I00S2018/TCS-4342/QUITEMAD-CMQuantum Science and Technologyhttps://doi.org/10.1088/2058-9565/ac1ab1Síinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/2619722026-05-22T06:33:51Z
dc.title.none.fl_str_mv Automatic design of quantum feature maps
title Automatic design of quantum feature maps
spellingShingle Automatic design of quantum feature maps
Altares-López, Sergio
Artificial intelligence
Automatic quantum classifier generation
Genetic algorithms
Optimization
Quantum computing
Quantum machine learning
title_short Automatic design of quantum feature maps
title_full Automatic design of quantum feature maps
title_fullStr Automatic design of quantum feature maps
title_full_unstemmed Automatic design of quantum feature maps
title_sort Automatic design of quantum feature maps
dc.creator.none.fl_str_mv Altares-López, Sergio
Ribeiro Seijas, Ángela
García-Ripoll, Juan José
author Altares-López, Sergio
author_facet Altares-López, Sergio
Ribeiro Seijas, Ángela
García-Ripoll, Juan José
author_role author
author2 Ribeiro Seijas, Ángela
García-Ripoll, Juan José
author2_role author
author
dc.contributor.none.fl_str_mv Agencia Estatal de Investigación (España)
Comunidad de Madrid
Altares-López, Sergio [0000-0002-0847-6113]
Ribeiro Seijas, Ángela [0000-0001-5807-8132]
García-Ripoll, Juan José [0000-0001-8993-4624]
Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]
dc.subject.none.fl_str_mv Artificial intelligence
Automatic quantum classifier generation
Genetic algorithms
Optimization
Quantum computing
Quantum machine learning
topic Artificial intelligence
Automatic quantum classifier generation
Genetic algorithms
Optimization
Quantum computing
Quantum machine learning
description We propose a new technique for the automatic generation of optimal ad-hoc anstze for classification by using quantum support vector machine. This efficient method is based on non-sorted genetic algorithm II multiobjective genetic algorithms which allow both maximize the accuracy and minimize the ansatz size. It is demonstrated the validity of the technique by a practical example with a non-linear dataset, interpreting the resulting circuit and its outputs. We also show other application fields of the technique that reinforce the validity of the method, and a comparison with classical classifiers in order to understand the advantages of using quantum machine learning.
publishDate 2021
dc.date.none.fl_str_mv 2021
2022
2022
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/10261/261972
https://api.elsevier.com/content/abstract/scopus_id/85115203955
url http://hdl.handle.net/10261/261972
https://api.elsevier.com/content/abstract/scopus_id/85115203955
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv #PLACEHOLDER_PARENT_METADATA_VALUE#
#PLACEHOLDER_PARENT_METADATA_VALUE#
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PGC2018-094792-B-I00
S2018/TCS-4342/QUITEMAD-CM
Quantum Science and Technology
https://doi.org/10.1088/2058-9565/ac1ab1
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
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