Michael Kölle, M.Sc.

Michael Kölle, M.Sc.

Lehrstuhl für Mobile und Verteilte Systeme

Ludwig-Maximilians-Universität München, Institut für Informatik

Oettingenstraße 67
80538 München

Raum E109

Telefon: +49 89 / 2180-9160

Fax: +49 89 / 2180-9148

Mail: michael.koelle@ifi.lmu.de

🔬 Research Interests

  • Quantum Artificial Intelligence
  • Multi-Agent Reinforcement Learning

🎓 Teaching (Assistance)

  • Rechnerarchitektur: SS22, SS21
  • Betriebssysteme: WS22/23, WS21/22, WS20/21
  • Quantum Applications: SS22
  • Intelligent Systems: WS22/23
  • Quantum Computing Programming: WS22/23, SS22, WS21/22, SS21

We are always looking for new tutors: Tutor:in für den Lehrbetrieb (m/w/d)

💡 Open thesis ideas

  • Quantum Auto Encoder using Variational Quantum Circuits
  • Evaluating Amplitude Embedding and Auto Encoded Angle Embedding for Variational Quantum Circuits and Convolutional Quantum Neural Networks
  • Multi Agent Quantum Reinforcement Learning using Quantum Boltzmann Machines
  • Evaluating Weight Shaping for Quantum Layers in Variational Quantum Circuits
  • Benchmarking State-of-the-Art Quantum Computing Frameworks (Simulators) for Quantum Machine Learning Applications
  • Optimizing/Building Quantum Circuits with Reinforcement Learning
  • Quantum Planning – Monte Carlo Rollouts with Quantum Walks

If you are interested in one of the advertised topics above or have your own ideas, send us: Anfrage Abschlussarbeiten

📖 Theses in progress

  • Quantum Enhanced Policy Gradient Methods for Reinforcement Learning – Mohamad Hgog
  • Embedding Classical Data for efficient Quantum Machine Learning – David Münzer
  • Multi-Agent Exploration through Peer Incentivization – Johannes Tochtermann
  • Efficient Data Embedding for offline Handwriting Recognition using Quantum Support Vector Machines – Leopold Bodendörfer

✅ Completed theses

  • Efficient embedding in Quantum Support Vector Machines using a specialized NISQ approach – Jonathan Wulf
  • A comparison of Generative Adversarial Networks and Variational Autoencoders for Density Estimation – Gerhard Stenzel
  • Anomaly Detection on Medical Images using Classification of Clustering Results – Sebastian Haugg
  • A Risk-Sensitive Approach for modeling the Hedging Portfolio Problem with Reinforcement Learning – Quentin Mathieu
  • Exploring the impact of markets on the credit assignment problem in a multi-agent environment – Zarah Zahreddin
  • Learning to Participate through Trading of Reward Shares – Tim Matheis

📚 Publications


  • A. Sedlmeier, M. Kölle, R. Müller, L. Baudrexel, and C. Linnhoff-Popien, „Quantifying Multimodality in World Models,“ in Proceedings of the 14th International Conference on Agents and Artificial Intelligence – Volume 1: ICAART,, 2022, pp. 367-374. doi:10.5220/0010898500003116
    author={Andreas Sedlmeier and Michael Kölle and Robert Müller and Leo Baudrexel and Claudia Linnhoff-Popien},
    title={Quantifying Multimodality in World Models},
    booktitle={Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,},