Generalized Directed Loop Method for Quantum Monte Carlo Simulations

TL;DR

This paper introduces a generalized directed loop method for quantum Monte Carlo simulations that optimizes update schemes by solving linear programming problems, extending applicability to various lattice models and improving efficiency.

Contribution

It develops a generalized framework for directed loops using linear programming, enabling bounce-free algorithms for diverse lattice models and proposing strategies to reduce autocorrelations.

Findings

Generalized directed loop algorithms are bounce-free over larger parameter regions.

Minimizing bounces alone does not always improve autocorrelation times.

Strategies to further reduce autocorrelations depend on model parameters and observables.

Abstract

Efficient quantum Monte Carlo update schemes called directed loops have recently been proposed, which improve the efficiency of simulations of quantum lattice models. We propose to generalize the detailed balance equations at the local level during the loop construction by accounting for the matrix elements of the operators associated with open world-line segments. Using linear programming techniques to solve the generalized equations, we look for optimal construction schemes for directed loops. This also allows for an extension of the directed loop scheme to general lattice models, such as high-spin or bosonic models. The resulting algorithms are bounce-free in larger regions of parameter space than the original directed loop algorithm. The generalized directed loop method is applied to the magnetization process of spin chains in order to compare its efficiency to that of previous directed loop schemes. In contrast to general expectations, we find that minimizing bounces alone does not always lead to more efficient algorithms in terms of autocorrelations of physical observables, because of the non-uniqueness of the bounce-free solutions. We therefore propose different general strategies to further minimize autocorrelations, which can be used as supplementary requirements in any directed loop scheme. We show by calculating autocorrelation times for different observables that such strategies indeed lead to improved efficiency; however we find that the optimal strategy depends not only on the model parameters but also on the observable of interest.