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Quantum kernel logistic regression based Newton method

Author

Listed:
  • Ning, Tong
  • Yang, Youlong
  • Du, Zhenye

Abstract

Kernel logistic regression (KLR) is a powerful machine learning model for classification, which has wide applications in pattern recognition. However, classical KLR algorithm is computationally expensive when dealing with big data sets. Since quantum technique exhibits a computational advantages in tackling machine learning problems, we devise a quantum KLR algorithm. Specifically, our algorithm makes use of quantum inner product estimation to prepare the desired state and then performs quantum singular value transformation based on the block-encoding framework to obtain the optimal model parameters. It is theoretically demonstrated that our algorithm has an exponential speedup over its classical counterpart.

Suggested Citation

  • Ning, Tong & Yang, Youlong & Du, Zhenye, 2023. "Quantum kernel logistic regression based Newton method," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 611(C).
    • Handle: RePEc:eee:phsmap:v:611:y:2023:i:c:s0378437123000092
    DOI: 10.1016/j.physa.2023.128454
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    References listed on IDEAS

    as
    1. Guo, Mingchao & Liu, Hailing & Li, Yongmei & Li, Wenmin & Gao, Fei & Qin, Sujuan & Wen, Qiaoyan, 2022. "Quantum algorithms for anomaly detection using amplitude estimation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 604(C).
    2. Liu, Hai-Ling & Yu, Chao-Hua & Wan, Lin-Chun & Qin, Su-Juan & Gao, Fei & Wen, Qiaoyan, 2022. "Quantum mean centering for block-encoding-based quantum algorithm," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 607(C).
    Full references (including those not matched with items on IDEAS)

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