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Machine Learning With Quantum Computers
Maria Schuld · Juan Carrasquilla

Mon Dec 06 05:00 AM -- 09:00 AM (PST) @

Quantum computing, a discipline that investigates how computing changes if we take into account quantum effects, has turned into an emerging technology that produced the first generation of hardware prototypes. In search of applications for these new devices, researchers turned to machine learning and found a wealth of exciting questions: Do machine learning algorithms gain a better computational complexity if we outsource parts of them to quantum computers? How does the problem of empirical risk minimisation change if our model class is made up of quantum algorithms? How does quantum hardware fit into AI pipelines? And, vice versa, can machine learning help us to study the behaviour of quantum systems?

In this tutorial we want to unpack these questions and sketch the landscape of preliminary answers found so far. For example, we will look at carefully constructed learning problems for which quantum computers have a provable complexity advantage, and motivate why it is so hard to make conclusive statements about more natural problem settings. We will explore how data can be represented as physical states of quantum systems, and how manipulating these systems leads to algorithms that are just kernel methods with a special kind of Hilbert space. We will see that quantum devices can be trained like neural networks, and that existing open-source software seamlessly integrates them into deep learning pipelines. Finally, we will understand how the deep connections between neural networks and quantum wave functions allow us to use machine learning techniques to understand quantum systems themselves.

The tutorial targets a broad audience, and no prior knowledge of physics is required.

Author Information

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.

Juan Carrasquilla (Vector Institute)

Juan Carrasquilla is a full-time researcher at the Vector Institute for Artificial Intelligence in Toronto, Canada, where he works on the intersection of condensed matter physics, quantum computing, and machine learning - such as combining quantum Monte Carlo simulations and machine learning techniques to analyze the collective behaviour of quantum many-body systems. He completed his PhD in Physics at the International School for Advanced Studies in Italy and has since held positions as a Postdoctoral Fellow at Georgetown University and the Perimeter Institute, as a Visiting Research Scholar at Penn State University, and was a Research Scientist at D-Wave Systems Inc. in Burnaby, British Columbia.

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