Deep learning of topological phase transitions from entanglement aspects for two-dimensional chiral p-wave superconductors

TL;DR

This paper demonstrates how deep learning applied to entanglement-related data can effectively identify topological phase transitions in two-dimensional chiral p-wave superconductors, revealing physically meaningful features and phase distinctions.

Contribution

It introduces practical entanglement-based input data forms for deep learning to study 2D topological superconductors and compares their effectiveness in phase transition detection.

Findings

Different input data forms influence predicted transition points.

Using complete entanglement quantities improves phase distinction.

Deep learning models learn physically meaningful features.

Abstract

Applying deep learning to investigate topological phase transitions (TPTs) becomes a useful method due to not only its ability to recognize patterns but also its statistical excellency to examine the amount of information carried by different types of data inputs. Among possible data types, entanglement-related quantities, such as Majorana correlation matrices (MCMs), one-particle entanglement spectra (OPES), and entanglement eigenvectors (OPEEs), have been proved effective, however, are to date mostly restricted to one dimension. Here, we propose practical input data forms based on those quantities to study TPTs and to compare the efficiency of each form on classic two-dimensional chiral $p$-wave superconductors via the deep learning approach. First, we find that different input forms, either matrices or tensors both originated from real MCMs, can affect the precise locations of the predicted transition points. Next, due to the complex nature of OPEEs, we extract three spatially dependent quantities from OPEEs, one related to the "intensity", and the other two related to "phases" of particle and hole components. We show that similar to taking OPES directly as inputs, solely using "intensity" quantity can only distinguish topological phases from trivial ones, whereas using either whole MCMs or complete OPEE-extracted quantities can provide sufficient information for deep learning to distinguish between phases of matter with different $U(1)$ gauges or Chern numbers. Finally, we discuss certain characteristic features in the deep learning approach and, in particular, they reveal that our trained models indeed learn physically meaningful features, which confirms the potential use even at high dimensions.