Article
Digital quantum simulation of an extended Agassi model: Using machine learning to disentangle its phase-diagram
Author/s | Sáiz Castillo, Álvaro
![]() ![]() ![]() García Ramos, José Enrique Arias Carrasco, José Miguel ![]() ![]() ![]() ![]() ![]() ![]() ![]() Lamata Manuel, Lucas ![]() ![]() ![]() ![]() ![]() ![]() Pérez Fernández, Pedro ![]() ![]() ![]() ![]() ![]() ![]() |
Department | Universidad de Sevilla. Departamento de Física Atómica, Molecular y Nuclear Universidad de Sevilla. Departamento de Física Aplicada III |
Publication Date | 2022 |
Deposit Date | 2022-12-23 |
Published in |
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Abstract | 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. |
Funding agencies | 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) |
Project ID. | PGC2018-095113-B-I00
![]() PID2019-104002GBC21 ![]() PID2019-104002GB-C22 ![]() PID2020-114687GBI00 ![]() P20-00617 ![]() P20-00764 ![]() P20-01247 ![]() UHU-1262561 ![]() US-1380840 ![]() |
Citation | 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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