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Quantum k-fold cross-validation for nearest neighbor classification algorithm

Author

Listed:
  • Li, Jing
  • Gao, Fei
  • Lin, Song
  • Guo, Mingchao
  • Li, Yongmei
  • Liu, Hailing
  • Qin, Sujuan
  • Wen, QiaoYan

Abstract

Cross-validation is one of the important tools in machine learning, which is generally used for performance evaluation. It uses different portions of the data to test and train a model on different iterations, which leads to a high computational cost. In this paper, we present a quantum version of k-fold cross-validation to choose a good parameter for the nearest neighbor classification algorithm with a threshold t, where the classification performance is estimated efficiently. With the help of amplitude amplification and estimation, the proposed quantum algorithm achieves a polynomial speedup on the number of samples over its classical counterpart.

Suggested Citation

  • Li, Jing & Gao, Fei & Lin, Song & Guo, Mingchao & Li, Yongmei & Liu, Hailing & Qin, Sujuan & Wen, QiaoYan, 2023. "Quantum k-fold cross-validation for nearest neighbor classification algorithm," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 611(C).
    • Handle: RePEc:eee:phsmap:v:611:y:2023:i:c:s0378437122009931
    DOI: 10.1016/j.physa.2022.128435
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    References listed on IDEAS

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    1. Jacob Biamonte & Peter Wittek & Nicola Pancotti & Patrick Rebentrost & Nathan Wiebe & Seth Lloyd, 2017. "Quantum machine learning," Nature, Nature, vol. 549(7671), pages 195-202, September.
    2. Ghosh, Anil K., 2006. "On optimum choice of k in nearest neighbor classification," Computational Statistics & Data Analysis, Elsevier, vol. 50(11), pages 3113-3123, July.
    3. 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).
    Full references (including those not matched with items on IDEAS)

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