Affiliation of Author(s):计算机科学与技术学院/人工智能学院/软件学院

Journal:Jilin Daxue Xuebao (Gongxueban)

Abstract:In this paper a quantum k-means algorithm is proposed by integrating the quantum paradigm to improve the efficiency of traditional k-means algorithm. First, each vector and k cluster centers are prepared to be in quantum superposition, which are then utilized to compute the similarities in parallel. Second, the quantum amplitude estimation algorithm is applied to convert the similarities into quantum bit. Finally, from the quantum bit the most similar center of the vector is obtained using the quantum algorithm for determining the minimum. Theoretical analysis shows that, compared with the traditional quantum algorithm, the time complexity of the quantum k-means algorithm decreases under given condition and the space complexity diminishes exponentially. © 2018, Editorial Board of Jilin University. All right reserved.

ISSN No.:1671-5497

Translation or Not:no

Date of Publication:2018-03-01

Co-author:Liu, Xue-Juan,Xu Juan,Duan, Bo-Jia

Correspondence Author:Yuan Jiabing