Nuclear Physics in the Era of Quantum Computing and Quantum Machine Learning

TL;DR

This paper explores the potential of quantum computing and quantum machine learning to advance low-energy nuclear physics, highlighting initial applications and future possibilities for computational improvements.

Contribution

It presents three specific examples demonstrating how quantum computing and machine learning could enhance nuclear physics calculations and experimental analysis.

Findings

Quantum methods can determine nuclear phase/shape.

Quantum algorithms estimate ground state energies.

Quantum techniques assist in particle identification.

Abstract

In this paper, the application of quantum simulations and quantum machine learning to solve low-energy nuclear physics problems is explored. The use of quantum computing to deal with nuclear physics problems is, in general, in its infancy and, in particular, the use of quantum machine learning in the realm of nuclear physics at low energy is almost nonexistent. We present here three specific examples where the use of quantum computing and quantum machine learning provides, or could provide in the future, a possible computational advantage: i) the determination of the phase/shape in schematic nuclear models, ii) the calculation of the ground state energy of a nuclear shell model-type Hamiltonian and iii) the identification of particles or the determination of trajectories in nuclear physics experiments.