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dc.contributor.authorDomingo Colomer, Laia 
dc.contributor.authorCarlo, G.
dc.contributor.authorBorondo, Florentino 
dc.contributor.otherUAM. Departamento de Químicaes_ES
dc.date.accessioned2023-02-09T11:07:19Z
dc.date.available2023-02-09T11:07:19Z
dc.date.issued2022-10-13
dc.identifier.citationPhysical Review E 106.4 (2022): L043301es_ES
dc.identifier.issn2470-0045 (print)es_ES
dc.identifier.issn2470-0053 (online)es_ES
dc.identifier.urihttp://hdl.handle.net/10486/706274
dc.description.abstractUniversal fault-tolerant quantum computers require millions of qubits with low error rates. Since this technology is years ahead, noisy intermediate-scale quantum (NISQ) computation is receiving tremendous interest. In this setup, quantum reservoir computing is a relevant machine learning algorithm. Its simplicity of training and implementation allows to perform challenging computations on today's available machines. In this Letter, we provide a criterion to select optimal quantum reservoirs, requiring few and simple gates. Our findings demonstrate that they render better results than other commonly used models with significantly less gates and also provide insight on the theoretical gap between quantum reservoir computing and the theory of quantum states' complexityes_ES
dc.format.extent7 pag.es_ES
dc.format.mimetypeapplication/pdfes_ES
dc.language.isoenges_ES
dc.publisherAmerican Physical Societyes_ES
dc.relation.ispartofPhysical Review E - Statistical, Nonlinear, Biological, and Soft Matter Physicses_ES
dc.rights© 2022 American Physical Societyes_ES
dc.subject.otherError Ratees_ES
dc.subject.otherFault-Tolerantes_ES
dc.subject.otherMachine Learning Algorithmses_ES
dc.subject.otherQuanta Computerses_ES
dc.subject.otherQuantum Statees_ES
dc.subject.otherReservoir Computinges_ES
dc.subject.otherSimple++es_ES
dc.subject.otherState Complexityes_ES
dc.titleOptimal quantum reservoir computing for the noisy intermediate-scale quantum eraes_ES
dc.typearticlees_ES
dc.subject.ecienciaQuímicaes_ES
dc.relation.publisherversionhttps://doi.org/10.1103/PhysRevE.106.L043301es_ES
dc.identifier.doi10.1103/PhysRevE.106.L043301es_ES
dc.identifier.publicationfirstpageL043301-1es_ES
dc.identifier.publicationissue4es_ES
dc.identifier.publicationlastpageL043301-7es_ES
dc.identifier.publicationvolume106es_ES
dc.relation.projectIDGobierno de España. PGC2018-093854-B-I00es_ES
dc.relation.projectIDGobierno de España. CEX2019-000904-Ses_ES
dc.relation.projectIDinfo:eu-repo/grantAgreement/EC/H2020/734557/EU//TraXes_ES
dc.type.versioninfo:eu-repo/semantics/publishedVersiones_ES
dc.rights.accessRightsopenAccesses_ES
dc.facultadUAMFacultad de Cienciases_ES
dc.institutoUAMInstituto de Ciencias Matemáticas (ICMAT)es_ES


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