Gaussian Mixture Model (GMM) is widely used in computer vision as a state-of-the-art clustering algorithm. This project proposes Quantum Gaussian Mixture Model (QGMM) for Quantum Clustering. According to the paper, QGMM outperforms classical GMM in every aspect of the estimations. Therefore, in this project, we'll implement QGMM and conduct some experiments to see if how fast it trains, how better it models the data, and what edge cases there are, compared with classical GMM.

Organization

Student

Sangyeon Kim

Mentors

  • Sumedh Ghaisas
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2019