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arXiv:2211.04614 · Physical review. E

Solving the nuclear pairing model with neural network quantum states

Mauro Rigo, Benjamin Hall, Morten Hjorth-Jensen, Alessandro Lovato, Francesco Pederiva

Published Feb 28, 2023• 14 citations
arXivHTMLPDF

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.

DOI
10.1103/physreve.107.025310

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