.png){kind=icon}
.png){kind=icon}
Return
Solving frustrated Ising models using tensor networks
.png){kind=icon}
OA
AI
DOI：10.1103/PhysRevResearch.3.013041.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Motivated by the recent success of tensor networks to calculate the residual entropy of spin ice and kagome Ising models, we develop a general framework to study frustrated Ising models in terms of infinite tensor networks that can be contracted using standard algorithms for infinite systems. This is achieved by reformulating the problem as local rules for configurations on overlapping clusters chosen in such a way that they relieve the frustration, i.e., that the energy can be minimized independently on each cluster. We show that optimizing the choice of clusters, including the weight on shared bonds, is crucial for the contractibility of the tensor networks, and we derive some basic rules and a linear program to implement them. We illustrate the power of the method by computing the residual entropy of a frustrated Ising spin system on a kagome lattice with next-next-nearest-neighbor interactions, vastly outperforming Monte Carlo methods in speed and accuracy. The extension to finite temperatures is briefly discussed.
Keywords:
LATTICE
PHASE
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
4.2
Papers:
7.6K
Citations:
2.7W