An Improved Penalty-Based Excited-State Variational Monte Carlo Approach with Deep-Learning Ansatzes.

ABSTRACT: We introduce several improvements to the penalty-based variational quantum Monte Carlo (VMC) algorithm for computing electronic excited states of Entwistle et al. [Nat. Commun. 14, 274 (2023)] and demonstrate that the accuracy of the updated method is competitive with other available excited-state VMC approaches. A theoretical comparison of the computational aspects of these algorithms is presented, where several benefits of the penalty-based method are identified. Our main contributions include an automatic mechanism for tuning the scale of the penalty terms, an updated form of the overlap penalty with proven convergence properties, and a new term that penalizes the spin of the wave function, enabling the selective computation of states with a given spin. With these improvements, a

SUBMITTER: Szabo PB 

PROVIDER: S-EPMC11428158 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature