Machine Learning Topological Phases with a Solid-state Quantum Simulator
Abstract
We report an experimental demonstration of a machine learning approach to identify exotic topological phases, with a focus on the three-dimensional chiral topological insulators. We show that the convolutional neural networks---a class of deep feed-forward artificial neural networks with widespread applications in machine learning---can be trained to successfully identify different topological phases protected by chiral symmetry from experimental raw data generated with a solid-state quantum simulator. Our results explicitly showcase the exceptional power of machine learning in the experimental detection of topological phases, which paves a way to study rich topological phenomena with the machine learning toolbox.
Cite
@article{arxiv.1905.03255,
title = {Machine Learning Topological Phases with a Solid-state Quantum Simulator},
author = {Wenqian Lian and Sheng-Tao Wang and Sirui Lu and Yuanyuan Huang and Fei Wang and Xinxing Yuan and Wengang Zhang and Xiaolong Ouyang and Xin Wang and Xianzhi Huang and Li He and Xiuying Chang and Dong-Ling Deng and Lu-Ming Duan},
journal= {arXiv preprint arXiv:1905.03255},
year = {2019}
}
Comments
Main text: 5 pages with 3 figures; supplemental materials: 8 pages with 4 figures and 2 tables; accepted at Physical Review Letters