Classifying extended, localized and critical states in quasiperiodic lattices via unsupervised learning

TL;DR

This paper demonstrates that unsupervised learning algorithms like DBSCAN and OPTICS can effectively classify phases in quasiperiodic models, matching traditional methods with over 98% accuracy, advancing phase diagram analysis in condensed matter physics.

Contribution

The study applies advanced unsupervised learning algorithms to classify quantum phases in quasiperiodic models, achieving high accuracy and demonstrating their effectiveness compared to traditional methods.

Findings

Unsupervised algorithms match traditional methods with over 98% accuracy.

DBSCAN and OPTICS successfully identify distinct phases in quasiperiodic models.

The approach offers a new tool for phase classification in condensed matter physics.

Abstract

Classification of quantum phases is one of the most important areas of research in condensed matter physics. In this work, we obtain the phase diagram of one-dimensional quasiperiodic models via unsupervised learning. Firstly, we choose two advanced unsupervised learning algorithms, Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Ordering Points To Identify the Clustering Structure (OPTICS), to explore the distinct phases of Aubry-Andr\'{e}-Harper model and quasiperiodic p-wave model. The unsupervised learning results match well with traditional numerical diagonalization. Finally, we compare the similarity of different algorithms and find that the highest similarity between the results of unsupervised learning algorithms and those of traditional algorithms has exceeded 98\%. Our work sheds light on applications of unsupervised learning for phase classification.