  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.
