Machine-Learning Study using Improved Correlation Configuration and Application to Quantum Monte Carlo Simulation
This work addresses phase classification in quantum spin models for researchers in condensed matter physics, but it is incremental as it applies an existing machine learning approach with a modified estimator.
The study tackled phase classification in spin models by using an improved estimator for correlation configurations in machine learning, achieving classification of the Berezinskii-Kosterlitz-Thouless transition in a quantum XY model with training data from a classical XY model.
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.