This introduction wants to give you an intuition for the main challenge of quantum machine learning: to find out how machine learning could potentially be innovated by technology based on (ever so subtly) different physics principles, if we only have access to small-scale prototypes and mathematical theory. You’ll also learn about two important paradigms in quantum computing, the near-term and fault-tolerant approaches, and about some popular research areas within QML.
Maria Schuld (Xanadu)
Maria Schuld works as a senior researcher for the Toronto-based quantum computing startup Xanadu, as well as for the Big Data and Informatics Flagship of the University of KwaZulu-Natal in Durban, South Africa, from which she received her PhD in 2017. She co-authored the book "Supervised Learning with Quantum Computers" (Springer 2018) and is a lead developer of the PennyLane software framework for quantum differentiable programming. Besides her pioneering research on the intersection of quantum computing and machine learning, Maria has a postgraduate degree in political science, and a keen interest in the interplay between data, emerging technologies and society.
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