Unified tensor network theory for frustrated classical spin models in two dimensions
Feng-Feng Song, Tong-Yu Lin, Guang-Ming Zhang

TL;DR
This paper introduces a unified tensor network approach to efficiently analyze frustrated classical spin models in two dimensions, capturing complex phase transitions by encoding emergent degrees of freedom.
Contribution
The work develops a novel tensor network framework that effectively relieves frustration and accurately determines phase transitions in 2D classical spin models.
Findings
Successfully applied to 2D frustrated XY models on various lattices
Accurately identified diverse phase transitions including BKT, second-order, and first-order
Demonstrated high efficiency and potential for broader frustrated systems
Abstract
Frustration is a ubiquitous phenomenon in many-body physics that influences the nature of the system in a profound way with exotic emergent behavior. Despite its long research history, the analytical or numerical investigations on frustrated spin models remain a formidable challenge due to their extensive ground state degeneracy. In this work, we propose a unified tensor network theory to numerically solve the frustrated classical spin models on various two-dimensional (2D) lattice geometry with high efficiency. We show that the appropriate encoding of emergent degrees of freedom in each local tensor is of crucial importance in the construction of the infinite tensor network representation of the partition function. The frustrations are thus relieved through the effective interactions between emergent local degrees of freedom. Then the partition function is written as a product of a…
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Taxonomy
TopicsQuantum many-body systems · Theoretical and Computational Physics · Physics of Superconductivity and Magnetism
