Embedded Files
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: e.greplova@tudelft.nl
EdX: Machine Learning for Semiconductor Quantum Devices
EdX: Machine Learning for Semiconductor Quantum Devices
ENROLL HERE and learn how to automate operation of your quantum device.
EdX: Development and Applications of Germanium Quantum Technologies
EdX: Development and Applications of Germanium Quantum Technologies
ENROLL HERE to learn about the role germanium devices play \ in the contemporary quantum technologies.
AP3751 AI for Physicists
AP3751 AI for Physicists
In the two masters programs at TU Delft (QIST and Applied Physics), me and the rest of the QMAI team are teaching the class that is introducing AI in the context of physics bottom-up with easy-to-digest physics example and hands-on group projects. Course website here.
Machine Learning for Quantum Experiments @ TU Delft
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 Jupyterbook
Machine Learning Jupyterbook
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.
Previously, I contribute to AP3681 Fairytales of Theoretical Physics, a masters course at TUDelft, where students get to work on exciting problems in contemporary theoretical physics.
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