Talks & Teaching

Few selected recorded scientific presentations on the recent research topics in QMAI:

Quantum Error Correction via Hamiltonian Learning, Perimeter Institute for Theoretical Physics (2019)

Predicting Phase Transitions in Many-Body Physics, Swiss Federal Institute of Technology Lausanne (2020)

Fully Automated Identification of 2D-material Samples, Virtual Science Forum (2019)

Learning Algorithms for Large Scale Quantum Systems, QTML2020

Understanding Quantum Matter Using Intelligent Machines, QWorld Webinar, 2021

Automated Control and Characterization of Quantum Devices, MLQ2021

Quantum Big Data: Where Condensed Matter Meets Quantum Computing, QRST Toronto, 2021

If you would like to have slides from any of my the non-recorded talks, please just get in touch:

Machine Learning for Quantum Experiments @ TU Delft

Casimir School course for PhD and master students working on quantum computing. Takes place yearsly on-site at TU Delft. All the course material, including the coding exercises can be found here.

Machine Learning Online Course

Our "Machine Learning for Scientists" course is available as a Jupyterbook as well as arXiv lecture notes. A shorter German version of the lecture notes is published in the Springer Essential Series, ISBN 978-3-658-32268-7.

Currently, I am teaching TN2513 Computational Science for bachelor students at TU Delft. We use inverted classroom approach , where students learn the methods by completing Jupyter Notebooks while asking questions via forum.

I also contribute to AP3681 Fairytales of Theoretical Physics, a masters course at TUDelft, where students get to work on exciting problems in contemporary theoretical physics.