Quantum K-nearest neighbor classification algorithm based on Hamming distance

TL;DR

This paper introduces a quantum K-nearest neighbor classification algorithm that leverages quantum computation to calculate Hamming distances in parallel and efficiently find the minimum, achieving quadratic speedup.

Contribution

The paper presents a novel quantum KNN algorithm utilizing Hamming distance and a new sub-algorithm for minimum search, improving computational efficiency.

Findings

Achieves quadratic speedup over classical KNN algorithms

Utilizes quantum parallelism for Hamming distance calculation

Provides a new sub-algorithm for minimum search in quantum context

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.