Winter term 2018/2019

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Machine learning in quantum physics

Quantum machine learning is an emerging research field. This first course focuses on the subfield where classical machine learning is applied in quantum physics. The goal of this course is to provide the students with the necessary skills to understand the main ideas and some details of the ongoing research in that area.


  1. Introduction to neural networks and deep learning
  2. Applications in quantum physics


Group seminar: Theoretical quantum science and technology

This is our group seminar. Interested students are always very welcome.

Group members will present and discuss their latest progress and results and give tutorial talks. Some talks will also be given by external visitors.


  • Quantum many-body physics,
  • Quantum information theory, 
  • Simulations of complex quantum systems,
  • Characterization and validation of quantum computing components, and
  • applied math.


See undefinedwww.mkliesch.eu/seminar.html.



Verantwortlich für den Inhalt: E-Mail sendenJens Bremer