A Non-stochastic Optimization Algorithm for Neural-network Quantum States

Abstract

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.

arXiv comment30 pages, 7 figures, and 1 table

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• Variational Quantum Imaginary Time Evolution for Matrix Product State Ansatz with Tests on Transcorrelated Hamiltonians
• Efficiency of the hidden fermion determinant states Ansatz in the light of different complexity measures
• Modal Backflow Neural Quantum States for Anharmonic Vibrational Calculations