Authors
Rigo, Mauro ; Hall, Benjamin; Hjorth-Jensen, Morten; Lovato, Alessandro; Pederiva, Francesco
Abstract
We present a variational Monte Carlo method that solves the nuclear many-body problem in theoccupation number formalism exploiting an artificial neural network representation of the groundstate 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 benchmarkthis approach against widely used nuclear many-body methods by solving a model used to describepairing 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 providesenergies that are in excellent agreement with the numerically-exact full configuration interactionvalues.