The seminar provides a high-level introduction to the emerging field of quantum machine learning, which investigates how quantum computers can be used to learn from data. After an overview of different ideas put forward to tackle this question, we will focus on the most popular approach: to train parametrised quantum circuits as if they were machine learning models. Participants will learn what we know about the expressivity, practical trainability and potential usefulness of these models, which turn out to be a mix between neural networks and support vector machines. Finally, we will have a look at the many future challenges and open questions in quantum machine learning, and discuss possible applications in High Energy Physics.
The seminar is followed by a hands-on tutorial in which we will train quantum circuits with the open-source software library PennyLane (https://indico.cern.ch/event/893116/)
M. Girone, M. Elsing, L. Moneta, M. Pierini