  We use machine learning techniques to solve the nuclear two-body bound state
problem, the deuteron. We use a minimal one-layer, feed-forward neural network
to represent the deuteron S- and D-state wavefunction in momentum space, and
solve the problem variationally using ready-made machine learning tools. We
benchmark our results with exact diagonalisation solutions. We find that a
network with 6 hidden nodes (or 24 parameters) can provide a faithful
representation of the ground state wavefunction, with a binding energy that is
within 0.1% of exact results. This exploratory proof-of-principle simulation
may provide insight for future potential solutions of the nuclear many-body
problem using variational artificial neural network techniques.

Nuclear Theory;Computational Physics