Measurement of entanglement entropy in the two-dimensional Potts model using wavelet analysis

TL;DR

This paper presents a novel wavelet-based method to measure entanglement entropy in the 2D Potts model by analyzing FK cluster configurations and their information loss during coarse-graining.

Contribution

The authors introduce a wavelet analysis technique to quantify entanglement entropy directly from FK cluster snapshots in the Potts model, linking image information loss to entanglement.

Findings

Wavelet analysis effectively measures entanglement entropy.

FK cluster configurations encode spin correlations.

Information loss correlates with entanglement entropy.

Abstract

We introduce a method to measure the entanglement entropy using a wavelet analysis. In the method we perform the two-dimensional Haar wavelet transform of configuration of Fortuin-Kasteleyn (FK) clusters. The configuration represents a direct snapshot of spin-spin correlations since spin degrees of freedom are traced out in FK representation. A snapshot of FK clusters loses image information at each coarse-graining process by the wavelet transform. We show that the loss of image information measures the entanglement entropy in the Potts model.