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Recent advances for quantum classifiers

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Abstract

Machine learning has achieved dramatic success in a broad spectrum of applications. Its interplay with quantum physics may lead to unprecedented perspectives for both fundamental research and commercial applications, giving rise to an emergent research frontier of quantum machine learning. Along this line, quantum classifiers, which are quantum devices that aim to solve classification problems in machine learning, have attracted tremendous attention recently. In this review, we give a relatively comprehensive overview for the studies of quantum classifiers, with a focus on recent advances. First, we will review a number of quantum classification algorithms, including quantum support vector machines, quantum kernel methods, quantum decision tree classifiers, quantum nearest neighbor algorithms, and quantum annealing based classifiers. Then, we move on to introduce the variational quantum classifiers, which are essentially variational quantum circuits for classifications. We will review different architectures for constructing variational quantum classifiers and introduce the barren plateau problem, where the training of quantum classifiers might be hindered by the exponentially vanishing gradient. In addition, the vulnerability aspect of quantum classifiers in the setting of adversarial learning and the recent experimental progress on different quantum classifiers will also be discussed.

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Authors and Affiliations

  1. Center for Quantum Information, Institute for Interdisciplinary Information Science, Tsinghua University, Beijing, 100084, China

    Weikang Li & Dong-Ling Deng

  2. Shanghai Qi Zhi Institute, Shanghai, 200232, China

    Dong-Ling Deng

Authors
  1. Weikang Li
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  2. Dong-Ling Deng
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Correspondence to Dong-Ling Deng.

Additional information

This work was supported by the Start-up Fund from Tsinghua University (Grant No. 53330300320), the National Natural Science Foundation of China (Grant No. 12075128), and the Shanghai Qi Zhi Institute. We would like to thank Zhide Lu, Wenjie Jiang, Daniel Lidar, Tobias Haug, Yuxuan Du, Peixin Shen, Sirui Lu, and Liwei Yu for helpful discussions and communications.

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Li, W., Deng, DL. Recent advances for quantum classifiers. Sci. China Phys. Mech. Astron. 65, 220301 (2022). https://doi.org/10.1007/s11433-021-1793-6

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