  The advent of Neural-network Quantum States (NQS) has significantly advanced
wave function ansatz research, sparking a resurgence in orbital space
variational Monte Carlo (VMC) exploration. This work introduces three
algorithmic enhancements to reduce computational demands of VMC optimization
using NQS: an adaptive learning rate algorithm, constrained optimization, and
block optimization. We evaluate the refined algorithm on complex multireference
bond stretches of $\rm H_2O$ and $\rm N_2$ within the cc-pVDZ basis set and
calculate the ground-state energy of the strongly correlated chromium dimer
($\rm Cr_2$) in the Ahlrichs SV basis set. Our results achieve superior
accuracy compared to coupled cluster theory at a relatively modest CPU cost.
This work demonstrates how to enhance optimization efficiency and robustness
using these strategies, opening a new path to optimize large-scale Restricted
Boltzmann Machine (RBM)-based NQS more effectively and marking a substantial
advancement in NQS's practical quantum chemistry applications.
