Tomita Yusuke, Shiina Kenta, Okabe Yutaka, Lee Hwee Kuan
College of Engineering, Shibaura Institute of Technology, Saitama 330-8570, Japan.
Department of Physics, Tokyo Metropolitan University, Hachioji, Tokyo 192-0397, Japan.
Phys Rev E. 2020 Aug;102(2-1):021302. doi: 10.1103/PhysRevE.102.021302.
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.
在自旋模型相分类的机器学习研究中，我们使用基于Fortuin-Kasteleyn表示的相关构型改进估计器，以替代普通的相关构型。使用改进估计器对经典自旋模型的相进行分类，该方法还应用于使用回路算法的量子蒙特卡罗模拟。我们分析了正方晶格上自旋-1/2量子XY模型的Berezinskii-Kosterlitz-Thouless（BKT）转变。我们使用机器学习方法对量子XY模型的BKT相和顺磁相进行分类。我们表明，可以通过使用经典XY模型的训练数据来对量子XY模型进行分类。
