@misc{10481/79792,
year = {2022},
month = {12},
url = {https://hdl.handle.net/10481/79792},
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 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.},
organization = {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},
organization = {ERDF A way of making Europe		
European Commission	SOMM17/6105/UGR	
ERDF/MINECO	UNHU-15CE-2848},
publisher = {American Physical Society},
title = {Digital quantum simulation of an extended Agassi model: Using machine learning to disentangle its phase-diagram},
doi = {10.1103/PhysRevC.106.064322},
author = {Sáiz, Álvaro and García Ramos, José Enrique and Arias, José Miguel and Lamata, Lucas and Pérez Fernández, Pedro},
}