A lot of QML research approaches the question you just tried to solve by interpreting quantum computations as machine learning models. Using the PennyLane software framework, we will show you practically how this can look like for generative and discriminative models. You’ll also see that we can compute gradients of our models, which means that they can be used as parts of deep learning pipelines.
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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