Worm Algorithm for Abelian Gauge-Higgs Models

TL;DR

The paper introduces the surface worm algorithm (SWA), a novel Monte Carlo method for simulating dual representations of Abelian gauge-Higgs models, outperforming traditional local updates across various parameters.

Contribution

It generalizes the worm algorithm to surface and loop updates for Abelian gauge-Higgs models, enhancing simulation efficiency.

Findings

SWA outperforms local Metropolis updates in efficiency.

SWA is effective across a wide parameter range.

The method facilitates better exploration of dual surface and loop configurations.

Abstract

We present the surface worm algorithm (SWA) which is a generalization of the Prokof'ev Svistunov worm algorithm to perform the simulation of the dual representation (surfaces and loops) of Abelian gauge-Higgs models on a lattice. We compare the SWA to a local Metropolis update in the dual representation and show that the SWA outperforms the local update for a wide range of parameters.