# Quantum Expectation-Maximization for Gaussian Mixture Models

@article{Kerenidis2020QuantumEF, title={Quantum Expectation-Maximization for Gaussian Mixture Models}, author={Iordanis Kerenidis and Alessandro Luongo and Anupam Prakash}, journal={ArXiv}, year={2020}, volume={abs/1908.06657} }

The Expectation-Maximization (EM) algorithm is a fundamental tool in unsupervised machine learning. It is often used as an efficient way to solve Maximum Likelihood (ML) estimation problems, especially for models with latent variables. It is also the algorithm of choice to fit mixture models: generative models that represent unlabelled points originating from $k$ different processes, as samples from $k$ multivariate distributions. In this work we define and use a quantum version of EM to fit a…

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