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.

本文提出了神经网络量子态的解释，以近似求解核物质的多体薛定谔方程，成功将 ANN 波函数与传统的二体和三体 Jastrow 函数和基本准确的 Green's 函数 Monte Carlo 结果进行了基准测试。