RT Journal Article
T1 Digital quantum simulation of an extended Agassi model: Using machine learning to disentangle its phase-diagram
A1 Sáiz, Álvaro
A1 García Ramos, José Enrique
A1 Arias Carrasco, José Miguel
A1 Lamata, Lucas
A1 Pérez Fernández, Pedro
AB A digital quantum simulation for the extended Agassi model is proposed using a quantum platform witheight 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-
PB American Physical Society
YR 2022
FD 2022
LK https://hdl.handle.net/10272/21372
UL https://hdl.handle.net/10272/21372
LA eng
NO Sáiz, Á., García-Ramos, J.-E., Arias, J. M., Lamata, L., & Pérez-Fernández, P. (2022). Digital quantum simulation of an extended Agassi model: Using machine learning to disentangle its phase-diagram. In Physical Review C (Vol. 106, Issue 6). American Physical Society (APS). https://doi.org/10.1103/physrevc.106.064322
NO This work was partially supported by the Consejería de Economía, Conocimiento, Empresas y Universidad de la Junta de Andalucía (Spain) under Groups No. FQM-160, FQM-177, and FQM-370, and under projects no. P20-00617, P20-00764, P20-01247, UHU-1262561, and US-1380840; by Grants No. PGC2018-095113-B-I00, PID2019-104002GB-C21, PID2019-104002GB-C22, and PID2020-114687GB-I00 funded by MCIN/AEI/10.13039/50110001103 and “ERDF A way of making Europe” and by ERDF, ref. SOMM17/6105/UGR. Resources supporting this work were provided by the CEAFMC and Universidad de Huelva High Performance Computer (HPC@UHU) funded by ERDF/MINECO Project No. UNHU-15CE-2848.
DS Repositorio Institucional de la Universidad de Huelva
RD 1 jun 2026