论文标题
用于二聚体模型的宽带采样和有效更新蒙特卡洛算法
Wide sampling and efficient updating Monte Carlo algorithms for dimer models
论文作者
论文摘要
量子二聚体模型是研究量子自旋系统和强相关物理的低能和有效模型。作为预测步骤，并且没有丧失一般性，我们通过Monte Carlo方法研究了正方形的经典二聚体。为了在二聚体模型中进行有效的状态更新，我们引入了一种高效的循环更新算法，该算法由称为“能量有向环路算法”的能量标准指导，并改进了口袋算法，以将它们与传统的有向环算法进行比较。相比之下，我们的能量循环算法增加了蒙特卡洛的收敛速度，并缩短了经典硬核二聚体模型中自动相关时间。改进的口袋算法和能量路径算法都可以用于品种二聚体模型中，并成功地迅速穿越了拓扑切片。
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.
