Quantum Expectation-Maximization Algorithm
Quantum Physics
2020-01-23 v1 Machine Learning
Machine Learning
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 (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.
Cite
@article{arxiv.1908.06655,
title = {Quantum Expectation-Maximization Algorithm},
author = {Hideyuki Miyahara and Kazuyuki Aihara and Wolfgang Lechner},
journal= {arXiv preprint arXiv:1908.06655},
year = {2020}
}
Comments
10 pages, 9 figures