Emmy-Noether Working Group: Theoretical Quantum System Design (TQSD)

Group leader and supervisor: Dr Janis Nötzel


The working group develops theoretical foundations of quantum system design.

This includes the application of analytical methods to problems motivated by practical system design. The research agenda includes the following four main topics:

  1. Emulation of future hybrid quantum communication networks. We have developed an emulator for this purpose. Our goal is to facilitate the interaction of theoretically and experimentally oriented groups and thus accelerate the design of new quantum communication networks.
  2. Quantum system design, in particular the interaction of the different resources that can be used for high data rates and reliable communication. We have demonstrated the impact of entanglement-assisted communication on the network layer of a hypothetical hybrid quantum communication network.
  3. Investigating new potential use cases enabled by adding quantum communication resources to current communication systems. Here we were able to prove the existence of extreme potential of entanglement-assisted modulation in hybrid communication systems.
  4. Secure message transmission over quantum channels.

Available Bachelor and Master Theses

1) Shape Recognition from Clustered Data in Three and More Dimensions

We are looking for students that are interested in the problem of fitting a geometric object (a paraboloid) to a given set of noisy data points. The complete project involves several steps, and is suitable for a bachelor- or master thesis. A subset of the complete set of steps can be selected to match the requirements for students aiming to write a bachelor thesis. The steps include the following parts:

  • Implementation of a known algorithm that fits paraboloids to three dimensional datasets
  • Testing of the robustness of the algorithm on noiseless and noisy artificial datasets
  • Extending the algorithm to at least four dimensional datasets
  • Adaption of real-world datasets to match the needed input format
  • Testing of the algorithm using real-world noisy datasets
  • Theoretical analysis of the robustness of the algorithm and its higher dimensional variants
  • Extension of the algorithm to apply clustering methods prior as a means of reducing the runtime
  • Clustered data points on an unknown paraboloid serve as input to the algorithm

Interested students should contact: Janis Nötzel (janis.noetzel@tum.de)

Thanks

We acknowledge funding by the DFG via grant NO 1129/2-1 and thank the MCQST for supporting us.