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, 2020
If you would like to have slides from any of my the non-recorded conferences, please just get in touch: e.greplova@tudelft.nl
Machine Learning 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 TN2413 Computational Science and contribute to AP3681 Fairytales of Theoretical Physics @TUDelft.