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Eliott Mamon

Research Assistant

Research Interests:

Quantum Machine Learning, Expressivity, Lie theory, t-designs, kernel methods

I am interested in various aspects regarding the theory of Quantum Machine Learning (QML), which essentially tries to elucidate how near-term or far-term quantum hardware could be of any practical use for practical machine learning applications. Mostly, I am trying to better understand what various mathematical tools can say about QML models, in terms of their expressivity, trainability, or generalization capability. I am especially keen on better grasping the tools that have a "geometrical" flavor, like Lie Theory (which is prominent in other areas of Physics), and what they can teach us about both classical and quantum learning models. Besides, I have a strong general interest in quantum foundations.

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