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arXiv · 2204.11252

Wide sampling and efficient updating Monte Carlo algorithms for dimer models

Yao Hongxu·Li Jiaze·Hou Jintao
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Abstract

Quantum dimer model is a low-energy and efficient model to study quantum spin systems and strong-correlated physics. As a foreseeing step and without loss of generality, we study the classical dimers on square lattice by means of Monte Carlo method. For efficient states updating in dimer model, we introduce a highly-efficient loop updating algorithm directed by energy criterion called energy directed loop algorithm and improve the pocket algorithm to compare them with the traditional directed loop algorithm. By comparisons, our energy directed loop algorithm increases the convergent speed of Monte Carlo and shorten the auto-correlated time in classical hard-core dimer model. Both the improved pocket algorithm and energy path algorithm can be used in varietal dimer models and succeed in traversing the topological sections rapidly.

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Yao Hongxu, Li Jiaze, Hou Jintao. 2022-04-24. Wide sampling and efficient updating Monte Carlo algorithms for dimer models. https://arxiv.org/abs/2204.11252

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