dc.creator | García Ramos, José Enrique | es |
dc.creator | Sáiz Castillo, Álvaro | es |
dc.creator | Arias Carrasco, José Miguel | es |
dc.creator | Lamata Manuel, Lucas | es |
dc.creator | Pérez Fernández, Pedro | es |
dc.date.accessioned | 2024-05-06T06:58:10Z | |
dc.date.available | 2024-05-06T06:58:10Z | |
dc.date.issued | 2024-05 | |
dc.identifier.citation | García Ramos, J.E., Sáiz Castillo, Á., Arias Carrasco, J.M., Lamata Manuel, L. y Pérez Fernández, P. (2024). Nuclear Physics in the Era of Quantum Computing andQuantum Machine Learning. Advanced Quantum Technologies, 202300219. https://doi.org/10.1002/qute.202300219. | |
dc.identifier.issn | 2511-9044 | es |
dc.identifier.uri | https://hdl.handle.net/11441/157610 | |
dc.description.abstract | In this paper, the application of quantum simulations and quantum machine learning is explored to solve problems in low-energy nuclear physics. The use of quantum computing to address nuclear physics problems is still in its infancy, and particularly, the application of quantum machine learning (QML) in the realm of low-energy nuclear physics is almost nonexistent. Three specific examples are presented where the utilization of quantum computing and QML provides, or can potentially provide in the future, a computational advantage: i) determining the phase/shape in schematic nuclear models, ii) calculating the ground state energy of a nuclear shell model-type Hamiltonian, and iii) identifying particles or determining trajectories in nuclear physics experiments. | es |
dc.description.sponsorship | Junta de Andalucía P20-00617, P20-00764, P20-01247, and US-1380840 | es |
dc.description.sponsorship | MCIN/AEI/10.13039/50110001103 and “ERDF A way of making Europe” PID2019-104002GB-C21, PID2019-104002GB-C22, PID2020-114687GB-I00, PID2022-136228NB-C21 and PID2022-136228NB-C22 | es |
dc.format | application/pdf | es |
dc.format.extent | 17 p. | es |
dc.language.iso | eng | es |
dc.publisher | Wiley | es |
dc.relation.ispartof | Advanced Quantum Technologies, 202300219. | |
dc.rights | Atribución 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | * |
dc.subject | nuclear models | es |
dc.subject | quantum machine learning | es |
dc.subject | quantum phase transitions | es |
dc.title | Nuclear Physics in the Era of Quantum Computing andQuantum Machine Learning | es |
dc.type | info:eu-repo/semantics/article | es |
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 Aplicada III | es |
dc.contributor.affiliation | Universidad de Sevilla. Departamento de Física Atómica, Molecular y Nuclear | es |
dc.relation.projectID | P20-00617 | es |
dc.relation.projectID | P20-00764 | es |
dc.relation.projectID | P20-01247 | es |
dc.relation.projectID | US-1380840 | es |
dc.relation.projectID | PID2019-104002GB-C21 | es |
dc.relation.projectID | PID2019-104002GB-C22 | es |
dc.relation.projectID | PID2020-114687GB-I00 | es |
dc.relation.projectID | PID2022-136228NB-C21 | es |
dc.relation.projectID | PID2022-136228NB-C22 | es |
dc.relation.publisherversion | https://dx.doi.org/10.1002/qute.202300219 | es |
dc.identifier.doi | 10.1002/qute.202300219 | es |
dc.journaltitle | Advanced Quantum Technologies | es |
dc.publication.initialPage | 202300219 | es |
dc.contributor.funder | Junta de Andalucía | 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 |