Fast approximate simulation of finite long-range spin systems

TL;DR

The paper introduces a new fast approximation method for simulating long-range spin systems, improving speed over traditional algorithms by modeling site evolution as independent Markov chains and analyzing associated errors.

Contribution

It presents a novel approximate simulation technique for spin systems that leverages independent Markov chain modeling and fast summation methods, enhancing computational efficiency.

Findings

Significant speedup over Doob-Gillespie algorithm

Detailed error analysis for site labeling and linear functions

Effective approximation of spin system evolution

Abstract

Tau leaping is a popular method for performing fast approximate simulation of certain continuous time Markov chain models typically found in chemistry and biochemistry. This method is known to perform well when the transition rates satisfy some form of scaling behaviour. In a similar spirit to tau leaping, we propose a new method for approximate simulation of spin systems which approximates the evolution of spin at each site between sampling epochs as an independent two-state Markov chain. When combined with fast summation methods, our method offers considerable improvement in speed over the standard Doob-Gillespie algorithm. We provide a detailed analysis of the error incurred for both the number of sites incorrectly labelled and for linear functions of the state.