Artículo
Digital quantum simulation of an extended Agassi model: Using machine learning to disentangle its phase-diagram
Autor/es | Sáiz Castillo, Álvaro
García Ramos, José Enrique Arias Carrasco, José Miguel Lamata Manuel, Lucas Pérez Fernández, Pedro |
Departamento | Universidad de Sevilla. Departamento de Física Atómica, Molecular y Nuclear Universidad de Sevilla. Departamento de Física Aplicada III |
Fecha de publicación | 2022 |
Fecha de depósito | 2022-12-23 |
Publicado en |
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Resumen | A digital quantum simulation for the extended Agassi model is proposed using a quantum platform with eight trapped ions. The extended Agassi model is an analytically solvable model including both short range pairing and ... A digital quantum simulation for the extended Agassi model is proposed using a quantum platform with eight trapped ions. The extended Agassi model is an analytically solvable model including both short range pairing and long range monopole-monopole interactions with applications in nuclear physics and in other many-body systems. In addition, it owns a rich phase diagram with different phases and the corresponding phase transition surfaces. The aim of this work is twofold: on one hand, to propose a quantum simulation of the model at the present limits of the trapped ions facilities and, on the other hand, to show how to use a machine learning algorithm on top of the quantum simulation to accurately determine the phase of the system. Concerning the quantum simulation, this proposal is scalable with polynomial resources to larger Agassi systems. Digital quantum simulations of nuclear physics models assisted by machine learning may enable one to outperform the fastest classical computers in determining fundamental aspects of nuclear matter. |
Agencias financiadoras | Ministerio de Economía y Competitividad (MINECO). España Ministerio de Ciencia e Innovación (MICIN). España Junta de Andalucía European Commission (EC). Fondo Europeo de Desarrollo Regional (FEDER) |
Identificador del proyecto | PGC2018-095113-B-I00
PID2019-104002GBC21 PID2019-104002GB-C22 PID2020-114687GBI00 P20-00617 P20-00764 P20-01247 UHU-1262561 US-1380840 |
Cita | Sáiz Castillo, Á., García Ramos, J.E., Arias Carrasco, J.M., Lamata Manuel, L. y Pérez Fernández, P. (2022). Digital quantum simulation of an extended Agassi model: Using machine learning to disentangle its phase-diagram. Physical Review C, 106 (6), 064322. https://doi.org/10.1103/physrevc.106.064322. |
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PhysRevC.106.064322.pdf | 3.737Mb | [PDF] | Ver/ | |