Observation of a Topological Phase Transition with Deep Neural Networks in spin-orbit-coupled fermions
POSTER
Abstract
Machine Learning (ML) techniques have emerged as a powerful tool in quantum matter research. In this poster, we highlight how ML algorithms enable us to analyze experimental data with unprecedented high sensitivities and identify topological phases even in the presence of unavoidable noises. To this end, we apply the trained network to low signal-to-noise-ratio (SNR) experimental data obtained in a symmetry-protected topological system of spin-orbit-coupled fermions [1]. The obtained phase diagram by ML is consistent with the results obtained by using the conventional method on higher SNR data. Our work highlights the potential of machine learning techniques to be used in various quantum systems.
*We acknowledge the generous support from the Research Grants Councils of Hong Kong, Guangdong Joint laboratory and the Harilela foundation through 16306119, 16302420, 16302821, 16306321, 16306922, C6005-17G, C6009-20G, RFS2122-6S04, and N-HKUST636-22, respectively.
Publication: Entong Zhao, Ting Hin Mak, Chengdong He, Zejian Ren, Ka Kwan Pak, Yu-Jun Liu and Gyu-Boong Jo, Optics Express 20 37786 (2022)
Presenters
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Yujun Liu
- Hong Kong University of Science and Technology
- The Hong Kong University of Science and Technology
- The Hong Kong University of Science and