Optimum and efficient sampling for variational quantum Monte Carlo

TL;DR

This paper introduces new sampling strategies for Variational Quantum Monte Carlo that improve computational efficiency and statistical accuracy by addressing limitations of traditional sampling methods.

Contribution

It provides a rigorous analysis of random errors, derives a lower error limit, and proposes an efficient sampling method that replaces heavy-tailed errors with normal errors.

Findings

Significantly increased computational efficiency.

Replaced infinite variance errors with normal distribution errors.

Validated method on first-row systems with improved results.

Abstract

Quantum mechanics for many-body systems may be reduced to the evaluation of integrals in 3N dimensions using Monte-Carlo, providing the Quantum Monte Carlo ab initio methods. Here we limit ourselves to expectation values for trial wavefunctions, that is to Variational quantum Monte Carlo. Almost all previous implementations employ samples distributed as the physical probability density of the trial wavefunction, and assume the Central Limit Theorem to be valid. In this paper we provide an analysis of random error in estimation and optimisation that leads naturally to new sampling strategies with improved computational and statistical properties. A rigorous lower limit to the random error is derived, and an efficient sampling strategy presented that significantly increases computational efficiency. In addition the infinite variance heavy tailed random errors of optimum parameters in conventional methods are replaced with a Normal random error, strengthening the theoretical basis of optimisation. The method is applied to a number of first-row systems, and compared with previously published results.