Solving the nuclear pairing model with neural network quantum states

• URL: http://arxiv.org/abs/2211.04614v1
• Date: Wed, 9 Nov 2022 00:18:01 GMT
• Title: Solving the nuclear pairing model with neural network quantum states
• Authors: Mauro Rigo, Benjamin Hall, Morten Hjorth-Jensen, Alessandro Lovato, Francesco Pederiva
• Abstract summary: We present a variational Monte Carlo method that solves the nuclear many-body problem in the occupation number formalism. A memory-efficient version of the reconfiguration algorithm is developed to train the network by minimizing the expectation value of the Hamiltonian.
• Score: 58.720142291102135
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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