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Topological data analysis and machine learning
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DOI：10.1080/23746149.2023.2202331.png)
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Abstract
En 中文
Topological 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
topological phase
Journal
IF:
13.8
Papers:
546
Citations:
6.3K