posted on 2023-11-28, 09:13authored byXiang Li, Jia-Cheng Huang, Guang-Ze Zhang, Hao-En Li, Chang-Su Cao, Dingshun Lv, Han-Shi Hu
Neural-network
quantum states (NQS) employ artificial neural networks
to encode many-body wave functions in a 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 compared with traditional quantum
chemistry methods. Here, we introduce a general nonstochastic 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 nonstochastic
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.