Quantum Expectation-Maximization Algorithm
This work addresses clustering in machine learning by extending quantum algorithms to GMMs, but it appears incremental as it builds directly on prior quantum k-means research.
The authors tackled the problem of clustering by proposing a quantum expectation-maximization algorithm for Gaussian mixture models, demonstrating its robustness and quantum speedup, and numerically showing GMM's advantage over k-means for non-trivial cluster data.
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.