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Machine Learning with Quantum Computers

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While it is widely believed that quantum computers will outperform their classical counterparts in various tasks, a clear advantage in the realm of machine learning has yet to be defined. In this talk, we will discuss the motivation behind using quantum computers for machine learning tasks and introduce, at a high level, some of the leading proposals for quantum machine learning algorithms.

Required reading: None.

Bios:

Maria works as a senior researcher for the Toronto-based quantum computing start-up Xanadu, where she specialises in the intersection of quantum physics and machine learning. She co-authored the book “Machine Learning with Quantum Computers” (Springer 2018/2021) as well as many papers on quantum machine learning and is a lead developer of the PennyLane software framework for quantum differentiable programming. Maria received her PhD degree in physics from the University of KwaZulu-Natal in 2017, but also holds a postgraduate degree in political science and spends some of her time understanding the use of machine learning tools for social sciences research.

Amira is a predoc researcher at IBM Zurich, focusing on quantum machine learning. More specifically, Amira is investigating the expressibilty of quantum circuits as applied to machine learning models. She is also a Google PhD fellow in quantum computing and is completing her PhD at the University of KwaZulu-Natal.

This talk is part of the Machine Learning Reading Group @ CUED series.

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