  Neural-network quantum states (NQS) employ artificial neural networks to
encode many-body wave functions in second quantization through variational
Monte Carlo (VMC). They have recently been applied to accurately describe
electronic wave functions of molecules and have shown the challenges in
efficiency comparing with traditional quantum chemistry methods. Here we
introduce a general non-stochastic optimization algorithm for NQS in chemical
systems, which deterministically generates a selected set of important
configurations simultaneously with energy evaluation of NQS. This method
bypasses the need for Markov-chain Monte Carlo within the VMC framework,
thereby accelerating the entire optimization process. Furthermore, this
newly-developed non-stochastic optimization algorithm for NQS offers comparable
or superior accuracy compared to its stochastic counterpart and ensures more
stable convergence. The application of this model to test molecules exhibiting
strong electron correlations provides further insight into the performance of
NQS in chemical systems and opens avenues for future enhancements.
