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arXiv · 2103.04253

Quantum K-nearest neighbor classification algorithm based on Hamming distance

Jing Li·Song Lin·Yu Kai·Gongde Guo
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Abstract

K-nearest neighbor classification algorithm is one of the most basic algorithms in machine learning, which determines the sample's category by the similarity between samples. In this paper, we propose a quantum K-nearest neighbor classification algorithm with Hamming distance. In this algorithm, quantum computation is firstly utilized to obtain Hamming distance in parallel. Then, a core sub-algorithm for searching the minimum of unordered integer sequence is presented to find out the minimum distance. Based on these two sub-algorithms, the whole quantum frame of K-nearest neighbor classification algorithm is presented. At last, it is shown that the proposed algorithm can achieve a quadratical speedup by analyzing its time complexity briefly.

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Jing Li, Song Lin, Yu Kai, Gongde Guo. 2021-03-07. Quantum K-nearest neighbor classification algorithm based on Hamming distance. https://doi.org/10.1007/s11128-021-03361-0

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