Digital quantum simulation of an extended Agassi model: Using machine learning to disentangle its phase-diagram

TL;DR

This paper presents a digital quantum simulation of the extended Agassi model using trapped ions and employs machine learning to accurately identify its phase diagram, demonstrating potential advantages over classical methods in nuclear physics research.

Contribution

It introduces a scalable quantum simulation approach for the extended Agassi model and integrates machine learning to analyze its phase diagram, advancing quantum simulation techniques.

Findings

Successful digital quantum simulation with eight trapped ions.

Machine learning accurately determines the phase diagram.

Potential to outperform classical computations in nuclear physics.

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 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.