Quantum Monte Carlo Loop Algorithm for the t-J Model

TL;DR

This paper introduces a generalized Quantum Monte Carlo loop algorithm for the t-J model, significantly reducing autocorrelation times and enabling efficient finite temperature simulations of complex quantum systems.

Contribution

It extends the loop algorithm to the t-J model via a novel mapping, improving simulation efficiency and addressing the sign problem.

Findings

Autocorrelation times reduced by orders of magnitude.

Algorithm is fully ergodic and works in continuous time.

First finite temperature results for various t-J related models.

Abstract

We propose a generalization of the Quantum Monte Carlo loop algorithm to the t-J model by a mapping to three coupled six-vertex models. The autocorrelation times are reduced by orders of magnitude compared to the conventional local algorithms. The method is completely ergodic and can be formulated directly in continuous time. We introduce improved estimators for simulations with a local sign problem. Some first results of finite temperature simulations are presented for a t-J chain, a frustrated Heisenberg chain, and t-J ladder models.