dc.creator | Barraza, Nancy Korina | es |
dc.creator | Alvarado Barrios, Gabriel Dario | es |
dc.creator | Peng, Jie | es |
dc.creator | Lamata Manuel, Lucas | es |
dc.creator | Solano, Enrique | es |
dc.creator | Albarrán Arriagada, Francisco | es |
dc.date.accessioned | 2022-09-23T12:52:07Z | |
dc.date.available | 2022-09-23T12:52:07Z | |
dc.date.issued | 2022 | |
dc.identifier.citation | Barraza, N.K., Alvarado Barrios, G.D., Peng, J., Lamata Manuel, L., Solano, E. y Albarrán Arriagada, F. (2022). Analog quantum approximate optimization algorithm. Quantum Science and Technology, 7, 045035. | |
dc.identifier.issn | 2058-9565 | es |
dc.identifier.uri | https://hdl.handle.net/11441/137335 | |
dc.description.abstract | We present an analog version of the quantum approximate optimization algorithm suitable for current quantum annealers. The central idea of this algorithm is to optimize the schedule function, which defines the adiabatic evolution. It is achieved by choosing a suitable parametrization of the schedule function based on interpolation methods for a fixed time, with the potential to generate any function. This algorithm provides an approximate result of optimization problems that may be developed during the coherence time of current quantum annealers on their way toward quantum advantage. | es |
dc.description.sponsorship | STCSM (2019SHZDZX01-ZX04 and 20DZ2290900) | es |
dc.description.sponsorship | Junta de Andalucía (P20-00617) | es |
dc.description.sponsorship | ANID SA77210018 and AFB 180001 | es |
dc.format | application/pdf | es |
dc.format.extent | 10 p. | es |
dc.language.iso | eng | es |
dc.publisher | IOP Publishing | es |
dc.relation.ispartof | Quantum Science and Technology, 7, 045035. | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Quantum annealers | es |
dc.subject | QAOA | es |
dc.subject | Adiabatic evolution | es |
dc.subject | Hybrid algorithms | es |
dc.title | Analog quantum approximate optimization algorithm | es |
dc.type | info:eu-repo/semantics/article | es |
dcterms.identifier | https://ror.org/03yxnpp24 | |
dc.type.version | info:eu-repo/semantics/submittedVersion | 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 | 2019SHZDZX01-ZX04 | es |
dc.relation.projectID | 20DZ2290900 | es |
dc.relation.projectID | P20-00617 | es |
dc.relation.projectID | SA77210018 | es |
dc.relation.publisherversion | https://dx.doi.org/10.1088/2058-9565/ac91f0 | es |
dc.identifier.doi | 10.1088/2058-9565/ac91f0 | es |
dc.journaltitle | Quantum Science and Technology | es |
dc.publication.volumen | 7 | es |
dc.publication.initialPage | 045035 | es |
dc.contributor.funder | Science and Technology Commission of Shanghai Municipality (STCSM) | es |
dc.contributor.funder | Agencia Nacional de Investigación y Desarrollo (ANID). Chile | es |
dc.contributor.funder | Junta de Andalucía | es |
dc.contributor.funder | AFB 180001 | es |