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Variational Monte Carlo calculations of $\mathbf{A\leq 4}$ nuclei with an artificial neural-network correlator ansatz
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abstract
The complexity of many-body quantum wave functions is a central aspect of several fields of physics and chemistry where non-perturbative interactions are prominent. Artificial neural networks (ANNs) have proven to be a flexible tool to approximate quantum many-body states in condensed matter and chemistry problems. In this work we introduce a neural-network quantum state ansatz to model the ground-state wave function of light nuclei, and approximately solve the nuclear many-body Schr\"odinger equation. Using efficient stochastic sampling and optimization schemes, our approach extends pioneering applications of ANNs in the field, which present exponentially-scaling algorithmic complexity. We compute the binding energies and point-nucleon densities of $A\leq 4$ nuclei as emerging from a leading-order pionless effective field theory Hamiltonian. We successfully benchmark the ANN wave function against more conventional parametrizations based on two- and three-body Jastrow functions, and virtually-exact Green's function Monte Carlo results.
Forward citations
Cited by 5 Pith papers
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Meson-Nucleus Bound States with Neural-Network Quantum States
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Light nuclear scattering from neural quantum states
Neural quantum states plus minimum principles compute elastic and inelastic neutron-deuteron scattering observables with conservative uncertainties, without time evolution.
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Medium-mass nuclei with neural quantum states
Pfaffian-Jastrow neural quantum states yield ground-state energies and charge radii for nuclei up to A=58, with weak p-wave terms reducing average energy error to ~3% while revealing Hamiltonian sensitivity and A^3 scaling.
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Hypernuclei with Neural Network Quantum States
Neural network quantum states, extended to include Lambda hyperons, reproduce hypernuclear separation energies to within roughly 9% and predict the observed proton-radius shrinkage in 7ΛLi.
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Fully-heavy multiquarks in neural-network quantum states
Neural-network quantum states are used to compute spectra of fully-heavy multiquarks in a non-relativistic quark model, claiming to overcome dimensionality issues with superior accuracy over prior approximations.
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