Eliott Mamon

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.