Quantum Machine Learning tutorial
A recording of this event is available at this link.
The full Q&A is available here.
SPEAKER: Maria Schuld (UKZN)
About the speaker:
Maria Schuld works as a senior researcher for the Toronto-based quantum computing start-up 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 theoretical physics 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 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 emerging technologies and society.
Tutorial:
The tutorial consists of a seminar on the first day , which will provide a high-level introduction to the emerging field of quantum machine learning, which investigates how quantum computers can be used to learn from data. This is then followed by a hands-on tutorial on the second day where participants will learn how to train parametrised quantum circuits as if they were machine learning models with the open-source software library PennyLane.
THIS EVENT WILL BE LIVE STREAMED AND RECORDED
For further questions, e-mail at mpp.tutorials@cern.ch
Local organising committee:
Thea Aarrestad (CERN)
Jennifer Ngadiuba (Caltech)
Maurizio Pierini (CERN)
Vladimir Loncar (CERN)
Sioni P. Summers (CERN)
Jean-Roch Vlimant (Caltech)
Quantum Technology Initiative coordinators:
Michael Doser (CERN)
Maria Girone (CERN)
Dorota Grabowska (CERN)
Alberto di Meglio (CERN)
Sofia Vallecorsa (CERN)