Binary tree summation Monte Carlo simulation for Potts models

TL;DR

This paper discusses the binary tree summation Monte Carlo algorithm for Potts models, highlighting its ability to simulate fractional states and compute thermodynamic quantities efficiently.

Contribution

It introduces the binary tree summation method, a novel Monte Carlo algorithm that enhances simulation capabilities for Potts models.

Findings

Simulates fractional number of Potts states

Provides partition function and thermodynamic quantities in a single run

Highlights features of the binary tree summation algorithm

Abstract

In this talk, we briefly comment on Sweeny and Gliozzi methods, cluster Monte Carlo method, and recent transition matrix Monte Carlo for Potts models. We mostly concentrate on a new algorithm known as "binary tree summation". Some of the most interesting features of this method will be highlighted - such as simulating fractional number of Potts states, as well as offering the partition function and thermodynamic quantities as functions of temperature in a single run.