Alessandro Lovato (Argonne National Lab)
Neural network quantum states for atomic nuclei

Artificial neural networks have proven to be a flexible tool to compactly represent quantum many-body states in condensed matter, chemistry, and nuclear physics problems, where non-perturbative interactions are prominent. In this talk, I will present a neural-network quantum ansatz suitable to represent the ground-state wave function of atomic nuclei in a systematically improvable fashion. Using efficient stochastic sampling and optimization schemes, we solve the nuclear many-body Schroedinger equation for a leading-order pionless effective field theory Hamiltonian. The binding energies and point-nucleon densities of nuclei with up to A=16 nucleons are benchmarked against accurate quantum Monte Carlo and hyperspherical harmonics results.