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

Aug 4, 2026

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A digital quantum simulation of the extended Agassi model is proposed using a quantum platform with eight trapped ions. The extended Agassi model is analytically solvable and combines short-range pairing with long-range monopole-monopole interactions, featuring a rich phase diagram with distinct phases and phase-transition surfaces. The work has two goals: implementing the simulation within trapped-ion facility limits and applying a machine-learning algorithm on top of the simulation to determine the system’s phase accurately. The approach is scalable with polynomial resources to larger systems.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages15

Reading Time38 min

AccessFree to download

Introduction

Motivation from solvable nuclear physics models

Quantum phase transitions and phase identification

Quantum simulation and ML-assisted phase determination

arXiv :2205 . 15122v3 [ quant-ph] 2 May 2023 Digital quantum simulation of an extended Agassi model: Using machine learning to disentangle its phase-diagram lvaro S􀀓aiz 1 , Jos􀀓e-Enrique Garc􀀓􀀐a-Ramos2 ;3 , Jos􀀓e Miguel Arias2 ;4 , Lucas Lamata2 ;4 , Pedro P􀀓erez-Fern􀀓andez 1 ;2 1 Departamento de F􀀓􀀐sica Aplicada III, Escuela T􀀓ecnica Superior de Ingenier􀀓􀀐a, Universidad de Sevilla, 41092 Sevilla, Spain. 2 Instituto Carlos I de F􀀓􀀐sica Te􀀓orica y Computacional, Universidad de Granada, Fuentenueva s/n, 18071 Granada, Spain 3 Departamento de Ciencias Integradas y Centro de Estudios Avanzados en F􀀓􀀐sica, Matem􀀓atica y Computaci􀀓on, Universidad de Huelva, 21071 Huelva, Spain 4 Departamento de F􀀓􀀐sica At􀀓omica, Molecular y Nuclear, Facultad de F􀀓􀀐sica, Universidad de Sevilla, Apartado 1065, E-41080 Sevilla, Spain (Dated: May 3, 2023) 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 di􀀋erent 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. I. INTRODUCTION Realistic physical systems, in general, and nuclear physics problems, in particular, are usually too complex to be solved exactly. Thus, many-body approximations have to be used to get reasonable approximate results. Frequently, the complexity of the problem is such that even the use of standard many-body approximations faces insuperable di􀀎culties. To tackle this issue, algebraic models, either for fermions or bosons, having only few degrees of freedom have been developed. These can be solved analytically in some particular scenarios and numerically in general cases, even for very large systems. These facts make them very useful to check approximations and to provide solid references to test more complex and fundamental calculations. Among this type of algebraic models, with relevance in Nuclear Physics, one can mention: the Elliot SU(3) model [1], which played a major role in the explanation of rotational structure emergence from single particle degrees of freedom in nuclei, the Interacting Boson Model (IBM) for the study of low-lying collective states in medium and heavy nuclei [2], or the Lipkin-Meshkov-Glick model (LMG) [3] introduced 􀀌rst to check approximate many-body techniques in nuclear models with many nucleons interacting via a monopole-monopole force. Those are examples of simple models that have been able to permeate other 􀀌elds of physics, e.g., condensed matter, quantum optics or molecular physics, due to its simplicity and ability to capture the key aspects of a large variety of physical systems. The Agassi model was proposed as a simple and exactly solvable version of the far more complex pairingplus-quadrupole model of nuclear physics [4] . It includes a combination of a two-level pairing model anda monopole-monopole interaction such as the one of the LMG model. The extended Agassi model was introduced in Ref. [5] and its main di􀀋erence with respect to the original model is the addition of a more general pairing interaction. The model presents a rich quantum phase diagram and, speci􀀌cally, gives rise to qua

FAQs

What platform is used for the digital quantum simulation of the extended Agassi model?

The simulation is implemented on a quantum platform using eight trapped ions.

What interactions define the extended Agassi model in this work?

It includes short-range pairing and long-range monopole-monopole interactions.

How is machine learning used in the phase determination task?

A machine-learning algorithm is applied on top of the quantum simulation to accurately determine the system’s phase based on simulation outcomes.

Aug 4, 2026

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