Digital quantum simulation of an extended Agassi model: Using machine learning to disentangle its phase-diagram
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Sáiz, Álvaro; García Ramos, José Enrique; Arias, José Miguel; Lamata, Lucas; Pérez Fernández, PedroEditorial
American Physical Society
Date
2022-12-21Referencia bibliográfica
Sáiz, Á... [et al.] (2022). Digital quantum simulation of an extended Agassi model: Using machine learning to disentangle its phase-diagram. Physical Review C, 106(6), 064322. DOI: [10.1103/PhysRevC.106.064322]
Sponsorship
Consejeria de Economia, Conocimiento, Empresas y Universidad de la Junta de Andalucia (Spain) FQM-160 FQM-177 FQM-370 P20-00617 P20-00764 P20-01247 UHU-1262561 US-1380840 MCIN/AEI PGC2018-095113-B-I00 PID2019-104002GB-C21 PID2019-104002GB-C22 PID2020-114687GB-I00; ERDF A way of making Europe European Commission SOMM17/6105/UGR ERDF/MINECO UNHU-15CE-2848Abstract
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 manybody
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.