dc.creator | Albarrán Arriagada, Francisco | es |
dc.creator | Retamal, Juan Carlos | es |
dc.creator | Solano, Enrique | es |
dc.creator | Lamata Manuel, Lucas | es |
dc.date.accessioned | 2021-04-29T12:33:04Z | |
dc.date.available | 2021-04-29T12:33:04Z | |
dc.date.issued | 2020 | |
dc.identifier.citation | Albarrán Arriagada, F., Retamal, J.C., Solano, E. y Lamata Manuel, L. (2020). Reinforcement learning for semi-autonomous approximate quantum eigensolver. Machine Learning: Science and Technology, 1 (1), 015002. | |
dc.identifier.issn | 2632-2153 | es |
dc.identifier.uri | https://hdl.handle.net/11441/108122 | |
dc.description.abstract | The characterization of an operator by its eigenvectors and eigenvalues
allows us to know its action over any quantum state. Here, we propose a protocol
to obtain an approximation of the eigenvectors of an arbitrary Hermitian quantum
operator. This protocol is based on measurement and feedback processes, which
characterize a reinforcement learning protocol. Our proposal is composed of two
systems, a black box named environment and a quantum state named agent. The role
of the environment is to change any quantum state by a unitary matrix UˆE = e
−iτOˆE
where OˆE is a Hermitian operator, and τ is a real parameter. The agent is a quantum
state which adapts to some eigenvector of OˆE by repeated interactions with the
environment, feedback process, and semi-random rotations. With this proposal, we
can obtain an approximation of the eigenvectors of a random qubit operator with
average fidelity over 90% in less than 10 iterations, and surpass 98% in less than 300
iterations. Moreover, for the two-qubit cases, the four eigenvectors are obtained with
fidelities above 89% in 8000 iterations for a random operator, and fidelities of 99%
for an operator with the Bell states as eigenvectors. This protocol can be useful to
implement semi-autonomous quantum devices which should be capable of extracting
information and deciding with minimal resources and without human intervention. | es |
dc.description.sponsorship | Programa de Financiamiento Basal para Centros Científicos y Tecnológicos de Excelencia (CONICYT)-FB0807 | es |
dc.description.sponsorship | EU FET-QMiCS (820505) y OpenSuperQ (820363) | es |
dc.description.sponsorship | Gobierno Vasco-IT986-16 | es |
dc.description.sponsorship | Ministerio de Ciencia e Innovación (MICIN), Agencia Estatal de Investigación de España (AEI) y Fondo Europeo de Desarrollo Regional (FEDER)-PGC2018-095113-B-I00 | es |
dc.format | application/pdf | es |
dc.format.extent | 15 p. | es |
dc.language.iso | eng | es |
dc.publisher | IOP Publishing | es |
dc.relation.ispartof | Machine Learning: Science and Technology, 1 (1), 015002. | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.title | Reinforcement learning for semi-autonomous approximate quantum eigensolver | es |
dc.type | info:eu-repo/semantics/article | es |
dcterms.identifier | https://ror.org/03yxnpp24 | |
dc.type.version | info:eu-repo/semantics/publishedVersion | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
dc.contributor.affiliation | Universidad de Sevilla. Departamento de Física Atómica, Molecular y Nuclear | es |
dc.relation.projectID | CONICYT-FB0807 | es |
dc.relation.projectID | QMiCS (820505) | es |
dc.relation.projectID | OpenSuperQ (820363) | es |
dc.relation.projectID | IT986-16 | es |
dc.relation.projectID | PGC2018-095113-B-I00 | es |
dc.relation.publisherversion | https://iopscience.iop.org/article/10.1088/2632-2153/ab43b4 | es |
dc.identifier.doi | 10.1088/2632-2153/ab43b4 | es |
dc.journaltitle | Machine Learning: Science and Technology | es |
dc.publication.volumen | 1 | es |
dc.publication.issue | 1 | es |
dc.publication.initialPage | 015002 | es |
dc.identifier.sisius | 21917496 | es |
dc.contributor.funder | Comisión Nacional de Investigación Científica y Tecnológica (CONICYT). Chile | es |
dc.contributor.funder | European Union (UE) | es |
dc.contributor.funder | Gobierno Vasco | es |
dc.contributor.funder | Ministerio de Ciencia e Innovación (MICIN). España | es |
dc.contributor.funder | Agencia Estatal de Investigación. España | es |
dc.contributor.funder | European Commission (EC). Fondo Europeo de Desarrollo Regional (FEDER) | es |