Welcome to Machine Learning for Quantum Experiments 2022
This site is an up-to-date resource for the course. All the learning materials and important updates will be published here on a daily basis.
To participate in the course, please bring a laptop. We will be using online teaching environment so installation of no specific packages beforehand is needed. For formalities and GS credits please check the official graduate school website for the course here.
This three day block course is taking place October 26-28 (10am - 5pm), on-site at TU Delft at the following locations:
Day 1: C1.190 (Building 58, van der Maasweg 9)
Day 2: C1.190 (Building 58, van der Maasweg 9)
Day 3: Room 0.96 (Building 23, Stevinweg 1)
Slides Day 1: Neural Networks and Supervised Learning in Spin System
Bonus Slides Day 1: Functions and Classes in Python
Slides Day 2: Automated Tuning of Quantum Devices with Neural Networks
Slides Day 3: Quantum Parameter Estimationn and Advanced Methods
You finished MLQE2022 - what to do next?
The best advice with ML is to learn by doing, try to complete all the course notebooks and really understand the code. PyTorch website has a great tutorials and documentation that will help you to learn more. Best practice is to find a small problem you want to solve with ML and try to write a code that does so from scratch.
On the content and theory side, these are some of our favorite resources:
first let me advertise a Jupyterbook we wrote with colleagues from Zurich, it contains more details for most things I discussed in the lecture and lots of extra exercises
wonderful book by Michael Nielsen on Neural Networks and Deep Learning is a place to head to for deeper conceptual understanding
Deep Learning for Computer Vision is a course ran by Fei-Fei Li at Stanford and has the best no-nonsense explanation of backpropagation, convolutions and neural net design
For relating ML to physics, you can check out arXiv pre-print of another book we wrote with colleagues on Modern Applications of Machine Learning in Quantum Physics and Chemistry
Another great ML for physics lectures are these by Florian Marquardt