  Neural network quantum states are a promising tool to analyze complex quantum
systems given their representative power. It can however be difficult to
optimize efficiently and effectively the parameters of this type of ansatz.
Here we propose a local optimization procedure which, when integrated with
stochastic reconfiguration, outperforms previously used global optimization
approaches. Specifically, we analyze both the ground state energy and the
correlations for the non-integrable tilted Ising model with restricted
Boltzmann machines. We find that sequential local updates can lead to faster
convergence to states which have energy and correlations closer to those of the
ground state, depending on the size of the portion of the neural network which
is locally updated. To show the generality of the approach we apply it to both
1D and 2D non-integrable spin systems.
