At the beginning, lecture and seminar will be given online due to COVID-19. Changes are possible depending on further developments. Changes will be announced via Moodle and via this website. All registered participants will be informed via email.
First lecture: Monday 20th April (online)
Lecture and Seminar on 1st June are canceled due to Pentecost.
Machine learning has become a useful and important tool in many areas of research including physics. This lecture aims to introduce the students to the basic concepts of classical machine learning (ML) and wil then extend to quantum machine learning (QML). Examples of applications of classical machine learning to physical problems from current research will be discussed. In addition, methods from quantum information will be introduced. Their combination with classical machine learning methods forms the new area of quantum machine learning.
- Neural networks
- Support vector machines
- Restricted Boltzmann machine
- Quantum annealing
- Amplitude amplification
Good knowledge of theoretical quantum mechanics is mandatory for this course. Basic knowledge of quantum information and programming skills are helpful but not required.
- Goodfellow, Bengio and Courville, "Deep Learning", MIT Press, 2016
- Lämmel and Cleve, "Künstliche Intelligenz", Hanser Verlag, 2008
- J. Biamonte et al., "Quantum Machine Learning", Nature 549, 195 (2017)
- Dunjko and Briegel, "Machine learning & artificial intelligence in the quantum domain", Rep. Prog. Phys. 81, 074001 (2018)
Students must achieve at least 40% of exercise points in order to participate at the exam. There will be a oral or written exam at the end of the semester depending on the number of participants and the current situation. Further details will be announced.