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DOE OSTI · 1970032

Solving the nuclear pairing model with neural network quantum states

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

In this work, 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 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 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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73 NUCLEAR PHYSICS AND RADIATION PHYSICS

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BibTeXRIS

Rigo, Mauro, Hall, Benjamin, Hjorth-Jensen, Morten, Lovato, Alessandro, Pederiva, Francesco. 2023-02-28. Solving the nuclear pairing model with neural network quantum states. https://doi.org/10.1103/physreve.107.025310

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