Work

Unsupervised learning of topological phase transitions using the Calinski-Harabaz index

Year: 2021

Type: article

Abstract: The authors propose a framework to deal with unsupervised machine learning of both topological phases and non-topological ones. From this, the Calinski-Harabaz index score can be used to probe phase transitions.

Source: Physical Review Research

DOI: 10.1103/physrevresearch.3.013074

Authors: Jielin Wang, Wanzhou Zhang, Tian Hua, Tzu-Chieh Wei

Institutions: Taiyuan University of Technology, State University of New York, Stony Brook University

Language: English

Topic: Topological Materials and Phenomena

Subfield: Atomic and Molecular Physics, and Optics

Field: Physics and Astronomy

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,625

Funders: National Science Foundation, National Natural Science Foundation of China, Shanxi Province Science Foundation for Youths

Awards: Toolkit for Characterizing Noisy Quantum Processors and Windows of Quantum Advantage, G1622695603, G2256257874, G7103415537

OpenAlex ID: W3093075634

FWCI: 4.228

Cites: 56

Cited by: 55

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