A light weight regularization for wave function parameter gradients in quantum Monte Carlo
Abstract
The parameter derivative of the expectation value of the energy, , is a key ingredient in variational quantum Monte Carlo (VMC) wave function optimization methods. In some cases, a na\"ive Monte Carlo estimate of this derivative suffers from an infinite variance which inhibits the efficiency of optimization methods that rely on a stable estimate of the derivative. In this work, we derive a simple regularization of the na\"ive estimator which is trivial to implement in existing VMC codes, has finite variance, and a negligible bias which can be extrapolated to zero bias with no extra cost. We use this estimator to construct an unbiased, finite variance estimation of for a multi-Slater-Jastrow trial wave function on the LiH molecule. This regularized estimator is a simple and efficient estimator of for VMC optimization techniques.
Cite
@article{arxiv.2002.01434,
title = {A light weight regularization for wave function parameter gradients in quantum Monte Carlo},
author = {Shivesh Pathak and Lucas K. Wagner},
journal= {arXiv preprint arXiv:2002.01434},
year = {2020}
}