English

Graphical Representations and Worm Algorithms for the O($N$) Spin Model

Statistical Mechanics 2023-11-14 v1

Abstract

We present a family of graphical representations for the O(NN) spin model, where N1N \ge 1 represents the spin dimension, and N=1,2,3N=1,2,3 corresponds to the Ising, XY and Heisenberg models, respectively. With an integer parameter 0N/20 \le \ell \le N/2, each configuration is the coupling of \ell copies of subgraphs consisting of directed flows and N2N -2\ell copies of subgraphs constructed by undirected loops, which we call the XY and Ising subgraphs, respectively. On each lattice site, the XY subgraphs satisfy the Kirchhoff flow-conservation law and the Ising subgraphs obey the Eulerian bond condition. Then, we formulate worm-type algorithms and simulate the O(NN) model on the simple-cubic lattice for NN from 2 to 6 at all possible \ell. It is observed that the worm algorithm has much higher efficiency than the Metropolis method, and, for a given NN, the efficiency is an increasing function of \ell. Beside Monte Carlo simulations, we expect that these graphical representations would provide a convenient basis for the study of the O(NN) spin model by other state-of-the-art methods like the tensor network renormalization.

Keywords

Cite

@article{arxiv.2306.12218,
  title  = {Graphical Representations and Worm Algorithms for the O($N$) Spin Model},
  author = {Longxiang Liu and Lei Zhang and Xiaojun Tan and Youjin Deng},
  journal= {arXiv preprint arXiv:2306.12218},
  year   = {2023}
}

Comments

10 pages, 6 figures

R2 v1 2026-06-28T11:10:40.736Z