Robust identification of topological phase transition by self-supervised machine learning approach

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

¹Physics Department, National Tsing Hua University, Hsinchu, Taiwan
²Physics Division, National Center for Theoretical Sciences, Taipei, Taiwan
³Frontier Center for Theory and Computation, National Tsing Hua University, Hsinchu, Taiwan
⁴Center for Quantum Technology, National Tsing Hua University, Hsinchu, Taiwan

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

We propose a systematic methodology to identify the topological phase transition through a self-supervised machine learning model, which is trained to correlate system parameters to the non-local observables in time-of-flight experiments of ultracold atoms. Different from the conventional supervised learning approach, where the predicted phase transition point is very sensitive to the training region and data labeling, our self-supervised learning approach identifies the phase transition point by the largest deviation of the predicted results from the known system parameters and by the highest confidence through a systematic shift of the training regions. We demonstrate the robust application of this approach results in various 1D and 2D exactly solvable models, using different input features (time-of-flight images, spatial correlation function or density–density correlation function). As a result, our self-supervised approach should be a very general and reliable method for many condensed matter or solid-state systems to observe new states of matters solely based on experimental measurements, even without a priori knowledge of the phase transition models.
This work has been published in New J. Phys. 23 083021 (2021).

Keywords: machine learning, topological phase transition, self-supervised learning