Topological data analysis and machine learning

被引：14

作者：

Leykam, Daniel ^{[1
,4
]}

Angelakis, Dimitris G. ^{[1
,2
,3
]}

机构：

[1] Natl Univ Singapore, Ctr Quantum Technol, Singapore, Singapore

[2] Tech Univ Crete, Sch Elect & Comp Engn, Iraklion, Greece

[3] AngelQ Quantum Comp, Singapore, Singapore

[4] Natl Univ Singapore, Ctr Quantum Technol, 3 Sci Dr 2, Singapore 117543, Singapore

来源：

ADVANCES IN PHYSICS-X | 2023年 / 8卷 / 01期

基金：

新加坡国家研究基金会;

关键词：

Machine learning; strongly correlated quantum systems; persistent homology; phase transition; quantum computing; condensed matter physics; topological phase; PERSISTENT HOMOLOGY; STABILITY; COMPLEXES; ENTROPY;

D O I：

10.1080/23746149.2023.2202331

中图分类号：

O4 [物理学];

学科分类号：

0702 ;

摘要：

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.

引用

页数：24

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