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arXiv · 2211.04614

Solving the nuclear pairing model with neural network quantum states

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

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Mauro Rigo, Benjamin Hall, Morten Hjorth-Jensen, Alessandro Lovato, Francesco Pederiva. 2022-11-09. Solving the nuclear pairing model with neural network quantum states. https://doi.org/10.1103/physreve.107.025310

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