Quantum K-nearest neighbor classification algorithm based on Hamming distance

• Published in: Quantum information processing Vol. 21; no. 1
• Main Authors: Li, Jing, Lin, Song, Yu, Kai, Guo, Gongde
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.01.2022; Springer Nature B.V
• Subjects: Algorithms; Classification; Data Structures and Information Theory; Machine learning; Mathematical Physics; Physics; Physics and Astronomy; Quantum Computing; Quantum Information Technology; Quantum Physics; Search algorithms; Spintronics; Quantum algorithm; nearest neighbor classification; Quantum machine learning
• ISSN: 1570-0755; 1573-1332
• DOI: 10.1007/s11128-021-03361-0

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.