Self-supervised ensemble learning: A universal method for phase transition classification of many-body systems

Chi-Ting Ho^1,2*, Daw-Wei Wang^1,2,3,4

¹Physics Department, National Tsing Hua University, Hsinchu, Taiwan
²Center for Theory and Computation, National Tsing Hua University, Hsinchu, Taiwan
³3Center for Quantum Technology, National Tsing Hua University, Hsinchu, Taiwan
⁴Physics Division, National Center for Theoretical Sciences, Taipei, Taiwan

* Presenter:Chi-Ting Ho, email:aeio20777@gmail.com

We develop a self-supervised ensemble learning (SSEL) method to accurately classify distinct types of phase transitions by analyzing the fluctuation properties of machine learning outputs. Employing the 2D Potts model and the 2D Clock model as benchmarks, we demonstrate the capability of SSEL in discerning first-order, second-order, and Berezinskii-Kosterlitz-Thouless transitions, using in situ spin configurations as the input features. Furthermore, we show that the SSEL approach can also be applied to investigate quantum phase transitions in 1D Ising and 1D
XXZ models upon incorporating quantum sampling. We argue that the SSEL model simulates a special state function with higher-order correlations between physical quantities, and hence provides richer information than previous machine learning methods. Consequently, our SSEL method can be generally applied to the identification/classification of phase transitions even without explicit knowledge of the underlying theoretical models.

Keywords: Machine Learning, Phase Transition, Critical Phenomena