Quantum Expectation-Maximization Algorithm
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.PDF Abstract
No code implementations yet. Submit your code now
Results from the Paper
Submit results from this paper to get state-of-the-art GitHub badges and help the community compare results to other papers.
No methods listed for this paper. Add relevant methods here