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

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

Physica A: Statistical Mechanics and its Applications, 2023, vol. 611, issue C

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.

Keywords: Quantum machine learning; Quantum computing; Cross-validation; Quantum nearest neighbor algorithm (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:611:y:2023:i:c:s0378437122009931

DOI: 10.1016/j.physa.2022.128435

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