Quantum K-nearest neighbor classification algorithm based on Hamming distance

Jing Li, Song Lin, Kai Yu, Gongde Guo

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 the Hamming distance. In this algorithm, quantum computation is utilized to obtain the Hamming distance in parallel at first. 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 significant speedup by analyzing its time complexity briefly.