吉林大学学报(工学版) ›› 2018, Vol. 48 ›› Issue (2): 539-544.doi: 10.13229/j.cnki.jdxbgxb20170051

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Quantum k-means algorithm

LIU Xue-juan, YUAN Jia-bin, XU Juan, DUAN Bo-jia   

  1. College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
  • Received:2017-01-14 Online:2018-03-01 Published:2018-03-01

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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.

Key words: artificial intelligence, clustering, quantum computation, quantum algorithm, quantum k-means

CLC Number: 

  • TP387
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