Topological data analysis and machine learning

Abstract

ABSTRACTTopological data analysis refers to approaches for systematically and reliably computing abstract ‘shapes’ of complex data sets. There are various applications of topological data analysis in life and data sciences, with growing interest among physicists. We present a concise review of applications of topological data analysis to physics and machine learning problems in physics including the unsupervised detection of phase transitions. We finish with a preview of anticipated directions for future research.

Keywords

• Machine learning
• strongly correlated quantum systems
• persistent homology
• phase transition
• quantum computing
• condensed matter physics