This section gives you a few insights into the mathematical apparatus of quantum computing. You will learn that quantum computers are samplers, and that we can describe expected outcomes of the samples using matrix-vector multiplications, that can be interpreted as algorithms or “quantum circuits”. You will also hear a few fleeting comments on why quantum theory is different from classical probability theory.
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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