  We solve the ground state of the deuteron using a variational neural network
ansatz for the wave function in momentum space. This ansatz provides a flexible
representation of both the $S$ and the $D$ states, with relative errors in the
energy which are within fractions of a percent of a full diagonalisation
benchmark. We extend the previous work on this area in two directions. First,
we study new architectures by adding more layers to the network and by
exploring different connections between the states. Second, we provide a better
estimate of the numerical uncertainty by taking into account the final
oscillations at the end of the minimisation process. Overall, we find that the
best performing architecture is the simple one-layer, state-independent
network. Two-layer networks show indications of overfitting, in regions that
are not probed by the fixed momentum basis where calculations are performed. In
all cases, the error associated to the model oscillations around the real
minimum is larger than the stochastic initialisation uncertainties. The
conclusions that we draw can be generalised to other quantum mechanics
settings.
