Machine learning in quantum physics - winter 18/19

Quantum machine learning is an emerging research field. This first course focuses on the subfield where classical machine learning is applied in quantum physics.


  1. Introduction to neural networks and deep learning

  2. Applications in quantum physics

Formal things

  • Parts of 2. will be given by the students in form of seminar talks

  • The course consists of 2 hours lecture and 2 hours tutorial class (Übung) per week. There will also be assignments, partially with simple coding exercises.

  • Prerequisites for attending are basic knowledge of linear algebra and quantum mechanics.


This course outline is a preliminary plan and still subject to changes.
Comments and suggestions are welcome (info@mkliesch.eu).