Article
Nuclear Physics in the Era of Quantum Computing andQuantum Machine Learning
Author/s | García Ramos, José Enrique
Sáiz Castillo, Álvaro Arias Carrasco, José Miguel Lamata Manuel, Lucas Pérez Fernández, Pedro |
Department | Universidad de Sevilla. Departamento de Física Aplicada III Universidad de Sevilla. Departamento de Física Atómica, Molecular y Nuclear |
Publication Date | 2024-05 |
Deposit Date | 2024-05-06 |
Published in |
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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 ... 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. |
Funding agencies | Junta de Andalucía Ministerio de Ciencia e Innovación (MICIN). España Agencia Estatal de Investigación. España European Commission (EC). Fondo Europeo de Desarrollo Regional (FEDER) |
Project ID. | P20-00617
P20-00764 P20-01247 US-1380840 PID2019-104002GB-C21 PID2019-104002GB-C22 PID2020-114687GB-I00 PID2022-136228NB-C21 PID2022-136228NB-C22 |
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. |
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