论文标题
使用改进的相关配置和应用于量子蒙特卡洛模拟的机器学习研究
Machine-Learning Study using Improved Correlation Configuration and Application to Quantum Monte Carlo Simulation
论文作者
论文摘要
我们将基于fortuin-kasteleyn表示相关配置的改进估计量作为替代自旋模型相位分类的机器学习研究中的普通相关配置的替代方案。使用改进的估计器对经典自旋模型的阶段进行分类，并且该方法也应用于使用环路算法的量子蒙特卡洛模拟。我们分析了平方晶格上自旋1/2量子XY模型的Berezinskii-Kosterlitz-thouless（BKT）过渡。我们使用机器学习方法对量子XY模型的BKT相和顺磁相分类。我们表明，可以使用经典XY模型的训练数据来执行量子XY模型的分类。
We use the Fortuin-Kasteleyn representation based improved estimator of the correlation configuration as an alternative to the ordinary correlation configuration in the machine-learning study of the phase classification of spin models. The phases of classical spin models are classified using the improved estimators, and the method is also applied to the quantum Monte Carlo simulation using the loop algorithm. We analyze the Berezinskii-Kosterlitz-Thouless (BKT) transition of the spin 1/2 quantum XY model on the square lattice. We classify the BKT phase and the paramagnetic phase of the quantum XY model using the machine-learning approach. We show that the classification of the quantum XY model can be performed by using the training data of the classical XY model.
