  Clustering algorithms are a cornerstone of machine learning applications.
Recently, a quantum algorithm for clustering based on the k-means algorithm has
been proposed by Kerenidis, Landman, Luongo and Prakash. Based on their work,
we propose a quantum expectation-maximization (EM) algorithm for Gaussian
mixture models (GMMs). The robustness and quantum speedup of the algorithm is
demonstrated. We also show numerically the advantage of GMM over k-means for
non-trivial cluster data.
