基于Hamming距离的量子K近邻分类算法
Quantum K-nearest neighbor classification algorithm based on Hamming distance
AI总结：
针对传统K近邻分类算法，提出基于Hamming距离的量子K近邻分类算法，利用量子计算并行获取距离并设计子算法寻找最小值，实现了二次加速。
摘要：
K近邻分类算法是机器学习中最基础的算法之一，通过样本间的相似度决定样本类别。本文提出一种基于Hamming距离的量子K近邻分类算法。该算法首先利用量子计算并行获取Hamming距离。随后，提出一个用于搜索无序整数序列最小值的核心子算法，以找出最小距离。基于这两个子算法，给出了K近邻分类算法的完整量子框架。最后，通过简要分析时间复杂度，表明所提算法可实现二次加速。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
