Solving the nuclear pairing model with neural network quantum states

TL;DR

This paper introduces a neural network-based variational Monte Carlo method to solve the nuclear many-body problem, achieving high accuracy and efficiency compared to traditional methods.

Contribution

The authors develop a neural network quantum state approach with a memory-efficient stochastic reconfiguration algorithm for nuclear pairing models.

Findings

Outperforms coupled-cluster method in accuracy

Achieves energies in excellent agreement with exact solutions

Demonstrates polynomial computational cost

Abstract

We present a variational Monte Carlo method that solves the nuclear many-body problem in the occupation number formalism exploiting an artificial neural network representation of the ground-state wave function. A memory-efficient version of the stochastic reconfiguration algorithm is developed to train the network by minimizing the expectation value of the Hamiltonian. We benchmark this approach against widely used nuclear many-body methods by solving a model used to describe pairing in nuclei for different types of interaction and different values of the interaction strength. Despite its polynomial computational cost, our method outperforms coupled-cluster and provides energies that are in excellent agreement with the numerically-exact full configuration interaction values.