Improved Optimization for the Neural-network Quantum States and Tests on the Chromium Dimer

• URL: http://arxiv.org/abs/2404.09280v3
• Date: Tue, 28 May 2024 07:18:13 GMT
• Title: Improved Optimization for the Neural-network Quantum States and Tests on the Chromium Dimer
• Authors: Xiang Li, Jia-Cheng Huang, Guang-Ze Zhang, Hao-En Li, Zhu-Ping Shen, Chen Zhao, Jun Li, Han-Shi Hu,
• Abstract summary: Neural-network Quantum States (NQS) has significantly advanced wave function ansatz research. This work introduces three algorithmic enhancements to reduce computational demands of VMC optimization using NQS.
• Score: 11.985673663540688
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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