2001/10/01 by Dario Bressanini, Gabriele Morosi, Massimo Mella · 1 citation
Computer Science · Physics and Astronomy · #Advanced Chemical Physics Studies #Quantum Computing Algorithms and Architecture #Quantum, superfluid, helium dynamics #physics.atm-clus
paper · pdf · doi:10.1063/1.1455618
Submitted for publication
arxiv created 2001/10/01 · openalex publication_date 2002/04/01 · arxiv updated 2009/12/01 · openalex created_date 2016/06/24 · openalex updated_date 2026/08/04
The energy variance optimization algorithm over a fixed ensemble of configurations in variational Monte Carlo often encounters problems of convergence. Being formally identical to a problem of fitting data, we re-examine it from a statistical maximum-likelihood point of view. We show that the assumption of an underlying Gaussian distribution of the local energy, implicit in the standard variance minimization scheme, is not theoretically nor practically justified, and frequently generates convergence problems. We propose alternative procedures for optimization of trial wave functions in quantum Monte Carlo and successfully test them by optimizing a trial wave function for the helium trimer.