Machine Learning Topological Phases with a Solid-State Quantum Simulator

Wenqian Lian, Sheng-Tao Wang, Sirui Lu, Yuanyuan Huang, Fei Wang, Xinxing Yuan, Wengang Zhang, Xiaolong Ouyang, Xin Wang, Xianzhi Huang, Li He, Xiuying Chang, Dong-Ling Deng, and Luming Duan
Phys. Rev. Lett. 122, 210503 – Published 31 May 2019
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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.

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  • Received 22 November 2018
  • Revised 5 May 2019

DOI:https://doi.org/10.1103/PhysRevLett.122.210503

© 2019 American Physical Society

Physics Subject Headings (PhySH)

Statistical Physics & ThermodynamicsCondensed Matter, Materials & Applied PhysicsQuantum Information, Science & Technology

Authors & Affiliations

Wenqian Lian1,*, Sheng-Tao Wang2,1,*, Sirui Lu1, Yuanyuan Huang1, Fei Wang1, Xinxing Yuan1, Wengang Zhang1, Xiaolong Ouyang1, Xin Wang1, Xianzhi Huang1, Li He1, Xiuying Chang1, Dong-Ling Deng1,†, and Luming Duan1,‡

  • 1Center for Quantum Information, IIIS, Tsinghua University, Beijing 100084, People’s Republic of China
  • 2Department of Physics, Harvard University, Cambridge, Massachusetts 02138, USA

  • *These authors contributed equally to this work.
  • dldeng@mail.tsinghua.edu.cn
  • lmduan@tsinghua.edu.cn

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Issue

Vol. 122, Iss. 21 — 31 May 2019

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