Loop-Cluster Coupling and Algorithm for Classical Statistical Models
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 -flow (loop) representation. We introduce a Loop-Cluster (LC) joint model of bond-occupation variables interacting with -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 -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.
Cite
@article{arxiv.1909.02719,
title = {Loop-Cluster Coupling and Algorithm for Classical Statistical Models},
author = {Lei Zhang and Manon Michel and Eren M. Elçi and Youjin Deng},
journal= {arXiv preprint arXiv:1909.02719},
year = {2020}
}