中国物理B ›› 2023, Vol. 32 ›› Issue (7): 70309-070309.doi: 10.1088/1674-1056/acb9fb

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Variational quantum semi-supervised classifier based on label propagation

Yan-Yan Hou(侯艳艳)1,2, Jian Li(李剑)3,†, Xiu-Bo Chen(陈秀波)4, and Chong-Qiang Ye(叶崇强)2   

  1. 1 College of Information Science and Engineering, ZaoZhuang University, Zaozhuang 277160, China;
    2 School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing 100876, China;
    3 School of Cyberspace Security, Beijing University of Posts and Telecommunications, Beijing 100876, China;
    4 Information Security Center, State Key Laboratory Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China
  • 收稿日期:2022-10-10 修回日期:2023-01-14 接受日期:2023-02-08 出版日期:2023-06-15 发布日期:2023-07-05
  • 通讯作者: Jian Li E-mail:lijian@bupt.edu.cn
  • 基金资助:
    Project supported by the Open Fund of Advanced Cryptography and System Security Key Laboratory of Sichuan Province (Grant No. SKLACSS-202108), the National Natural Science Foundation of China (Grant No. U162271070), Scientific Research Fund of Zaozhuang University (Grant No. 102061901).

Variational quantum semi-supervised classifier based on label propagation

Yan-Yan Hou(侯艳艳)1,2, Jian Li(李剑)3,†, Xiu-Bo Chen(陈秀波)4, and Chong-Qiang Ye(叶崇强)2   

  1. 1 College of Information Science and Engineering, ZaoZhuang University, Zaozhuang 277160, China;
    2 School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing 100876, China;
    3 School of Cyberspace Security, Beijing University of Posts and Telecommunications, Beijing 100876, China;
    4 Information Security Center, State Key Laboratory Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China
  • Received:2022-10-10 Revised:2023-01-14 Accepted:2023-02-08 Online:2023-06-15 Published:2023-07-05
  • Contact: Jian Li E-mail:lijian@bupt.edu.cn
  • Supported by:
    Project supported by the Open Fund of Advanced Cryptography and System Security Key Laboratory of Sichuan Province (Grant No. SKLACSS-202108), the National Natural Science Foundation of China (Grant No. U162271070), Scientific Research Fund of Zaozhuang University (Grant No. 102061901).

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摘要： Label propagation is an essential semi-supervised learning method based on graphs, which has a broad spectrum of applications in pattern recognition and data mining. This paper proposes a quantum semi-supervised classifier based on label propagation. Considering the difficulty of graph construction, we develop a variational quantum label propagation (VQLP) method. In this method, a locally parameterized quantum circuit is created to reduce the parameters required in the optimization. Furthermore, we design a quantum semi-supervised binary classifier based on hybrid Bell and Z bases measurement, which has a shallower circuit depth and is more suitable for implementation on near-term quantum devices. We demonstrate the performance of the quantum semi-supervised classifier on the Iris data set, and the simulation results show that the quantum semi-supervised classifier has higher classification accuracy than the swap test classifier. This work opens a new path to quantum machine learning based on graphs.

关键词: semi-supervised learning, variational quantum algorithm, parameterized quantum circuit

Abstract: Label propagation is an essential semi-supervised learning method based on graphs, which has a broad spectrum of applications in pattern recognition and data mining. This paper proposes a quantum semi-supervised classifier based on label propagation. Considering the difficulty of graph construction, we develop a variational quantum label propagation (VQLP) method. In this method, a locally parameterized quantum circuit is created to reduce the parameters required in the optimization. Furthermore, we design a quantum semi-supervised binary classifier based on hybrid Bell and Z bases measurement, which has a shallower circuit depth and is more suitable for implementation on near-term quantum devices. We demonstrate the performance of the quantum semi-supervised classifier on the Iris data set, and the simulation results show that the quantum semi-supervised classifier has higher classification accuracy than the swap test classifier. This work opens a new path to quantum machine learning based on graphs.

Key words: semi-supervised learning, variational quantum algorithm, parameterized quantum circuit

中图分类号:  (Quantum algorithms, protocols, and simulations)

  • 03.67.Ac
03.67.Lx (Quantum computation architectures and implementations)