Quantum Machine Learning tutorial

3 Feb 2021, 11:00 → 4 Feb 2021, 17:00 Europe/Zurich

222/R-001 (CERN)

Jennifer Ngadiuba (CERN), Thea Aarrestad (CERN), Maurizio Pierini (CERN), Jean-Roch Vlimant (California Institute of Technology (US))

A recording of this event is available at this link.

The full Q&A is available here.

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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)

Wednesday, 3 February

1 Data-science seminar: Quantum Machine Learning

Speaker: Maria Schuld (UKZN)

Seminar

Thursday, 4 February

2 Welcome and recap

Speaker: Maria Schuld

3 Part I: Classical machine learning with automatic differentiation

Learning objectives:

• Be able to explain the concept of automatic differentiation
• Be able to train a simple linear model using automatic differentiation

Speaker: Maria Schuld

4 Part II: Differentiable quantum computing

Learning objectives:

• Be able to implement a variational quantum circuit in PennyLane
• Compute the gradient of a variational quantum circuit
• Train a variational quantum circuit like a machine learning model

Speaker: Maria Schuld

5 Break (there will be an optional task for the break)

Speaker: Maria Schuld

6 Part III: Quantum gradients on remote devices

Learning objectives:

• Be able to name and explain three different ways to compute quantum gradients
• Understand why parameter-shift rules are needed for hardware
• Be able to compute a quantum gradient on a remote backend

Speaker: Maria Schuld

7 Final words

Speaker: Maria Schuld