Solving the nuclear pairing model with neural network quantum states

Phys Rev E. 2023 Feb;107(2-2):025310. doi: 10.1103/PhysRevE.107.025310.


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.

PMID:36932590 | DOI:10.1103/PhysRevE.107.025310

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