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Quantum Expectation-Maximization Algorithm
Hideyuki Miyahara, K. Aihara, W. Lechner·August 19, 2019·DOI: 10.1103/PhysRevA.101.012326
Computer SciencePhysicsMathematics
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Abstract
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 algorithm for Gaussian mixture models (GMMs). The robustness and quantum speedup of the algorithm are shown. We also show numerically the advantage of GMM over $k$-means algorithm for nontrivial cluster data.