Variational Monte Carlo calculations of $\mathbf{A\leq 4}$ nuclei with an artificial neural-network correlator ansatz

TL;DR

This paper introduces a neural-network quantum state ansatz to model the ground-state wave functions of light nuclei, enabling approximate solutions to the nuclear many-body Schrödinger equation with improved efficiency and accuracy.

Contribution

The work presents a novel neural-network based wave function ansatz for light nuclei, extending ANN applications to nuclear physics and benchmarking against established methods.

Findings

Accurately computed binding energies and densities for $A\, extless=4$ nuclei.

Achieved good agreement with Green's function Monte Carlo results.

Demonstrated efficiency over traditional exponential-scaling algorithms.

Abstract

The complexity of many-body quantum wave functions is a central aspect of several fields of physics and chemistry where non-perturbative interactions are prominent. Artificial neural networks (ANNs) have proven to be a flexible tool to approximate quantum many-body states in condensed matter and chemistry problems. In this work we introduce a neural-network quantum state ansatz to model the ground-state wave function of light nuclei, and approximately solve the nuclear many-body Schr\"odinger equation. Using efficient stochastic sampling and optimization schemes, our approach extends pioneering applications of ANNs in the field, which present exponentially-scaling algorithmic complexity. We compute the binding energies and point-nucleon densities of $A\leq 4$ nuclei as emerging from a leading-order pionless effective field theory Hamiltonian. We successfully benchmark the ANN wave function against more conventional parametrizations based on two- and three-body Jastrow functions, and virtually-exact Green's function Monte Carlo results.