Loop-Cluster Coupling and Algorithm for Classical Statistical Models

TL;DR

This paper introduces a unified Loop-Cluster model and algorithm for Potts systems, connecting different representations and enabling efficient computation of physical quantities and geometric properties.

Contribution

It presents a novel Loop-Cluster joint model and algorithm that unify various representations of Potts models, offering new insights and computational tools.

Findings

The LC algorithm shares universality with Swendsen-Wang.

It enables measurement of physical quantities across different representations.

Constructs a hierarchy of geometric objects related to FK and $q$-flow clusters.

Abstract

Potts spin systems play a fundamental role in statistical mechanics and quantum field theory, and can be studied within the spin, the Fortuin-Kasteleyn (FK) bond or the $q$-flow (loop) representation. We introduce a Loop-Cluster (LC) joint model of bond-occupation variables interacting with $q$-flow variables, and formulate a LC algorithm that is found to be in the same dynamical universality as the celebrated Swendsen-Wang algorithm. This leads to a theoretical unification for all the representations, and numerically, one can apply the most efficient algorithm in one representation and measure physical quantities in others. Moreover, by using the LC scheme, we construct a hierarchy of geometric objects that contain as special cases the $q$-flow clusters and the backbone of FK clusters, the exact values of whose fractal dimensions in two dimensions remain as an open question. Our work not only provides a unified framework and an efficient algorithm for the Potts model, but also brings new insights into rich geometric structures of the FK clusters.