Nuclei with up to $\boldsymbol{A=6}$ nucleons with artificial neural network wave functions

Abstract

The ground-breaking works of Weinberg have opened the way to calculations of atomic nuclei that are based on systematically improvable Hamiltonians. Solving the associated many-body Schrödinger equation involves non-trivial difficulties, due to the non-perturbative nature and strong spin-isospin dependence of nuclear interactions. Artificial neural networks have proven to be able to compactly represent the wave functions of nuclei with up to A = 4 nucleons. In this work, we extend this approach to 6 Li and 6 He nuclei, using as input a leading-order pionless effective field theory Hamiltonian. We successfully benchmark their binding energies, point-nucleon densities, and radii with the highly-accurate hyperspherical harmonics method.

Bootstrapping the deuteron

“…Some noticeable progress has been made very recently in this direction. Inspired by the huge success of artificial intelligence (AI), new variational methods based on artificial neural networks (ANNs) are put forward for few-nucleon systems, incorporating the latest advances in AI into conventional variational methods [3][4][5]. Also, lots of interests are stimulated in developing new theoretical methods on quantum devices, encouraged by the public accessibility of quantum computing clouds via the internet [6][7][8][9][10][11][12][13][14][15][16][17][18][19][20].…”