Machine-learning study using improved correlation configuration and application to quantum Monte Carlo simulation

Yusuke Tomita, Kenta Shiina, Yutaka Okabe, Hwee Kuan Lee

Research output: Contribution to journalArticle

Abstract

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.

Original languageEnglish
Article number021302
JournalPhysical Review E
Volume102
Issue number2
DOIs
Publication statusPublished - 2020 Aug

ASJC Scopus subject areas

  • Statistical and Nonlinear Physics
  • Statistics and Probability
  • Condensed Matter Physics

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