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
  • Reinforcement Learning
  • Multi-Agent Systems

🎓 Teaching (Assistance)

  • Rechnerarchitektur: SS24, SS23, SS22 (Lehrpreis beste Bachelor Vorlesung), SS21
  • Betriebssysteme: WS23/24, WS22/23 (Lehrpreis beste Bachelor Vorlesung), WS21/22, WS20/21
  • Quantum Applications: SS23, SS22
  • Intelligent Systems: WS23/24, WS22/23
  • Quantum Computing Programming: SS24, WS23/24, SS23, WS22/23, SS22, WS21/22, SS21

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

💡 Open thesis ideas

  • Offline Quantum Reinforcement Learning using Metaheuristic Optimization Strategies
  • Evaluating Robustness using Adversarial Attacks on Quantum Reinforcement Learning Systems

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

📖 Theses in progress

  • Determining Semantic Links in Product Data Using Quantum Restricted Boltzmann Machines – Simon Hehnen
  • Distributed Quantum Machine Learning – Training and Evaluating a machine learning model on a distributed quantum computing simulator – Kian Izadi
  • Learning Independent Multi-Agent Flocking Behavior With Reinforcement Learning – Gregor Reischl
  • Investigating the Lottery Ticket Hypothesis for Variational Quantum Circuits – Leonhard Klingert
  • Architectural Influence on Variational Quantum Circuits in Multi-Agent Reinforcement Learning: Evolutionary Strategies for Optimization – Karola Schneider
  • QUBO Generation for (MAX-)3SAT Using Generative AI Methods – Philippe Wehr
  • Quantum Reinforcement Learning via Parameterized Quantum Walks – Sabrina Egger
  • Evaluating Parameter-Based Training Performance of Neural Network and Variational Quantum Circuits – Alexander Feist
  • Evaluating Mutation Techniques in Genetic Algorithm-Based Quantum Circuit Synthesis – Tom Bintener
  • Exploring QuGANs for Realistic Graph Generation – An Exploratory Study – Florian Burger

✅ Completed theses

  • Masked Autoencoders for Unsupervised Anomalous Sound Detection – Florian Reusch
  • Evaluating Metaheuristic Optimization Algorithms for Quantum Reinforcement Learning – Daniel Seidl
  • Influencing behavior through reward manipulation in multi-agent reinforcement-learning – Llewellyn Hochhauser
  • Parameter reduction with quantum circuits – The potential of Quantum Proximal Policy Optimization – Timo Witter
  • Anomalous Sound Detection with Multimodal Embeddings – Lara Lanz
  • A Reinforcement-Learning Environment for purposeful Quantum Circuit Design and Quantum State Preparation – Tom Schubert
  • Efficient unsupervised quantum anomaly detection using one-class support vector machines – Afrae Ahouzi
  • Exploring Multi-Agent Reinforcement Learning Strategies in a Predator-Prey Setting – Yannick Erpelding
  • Quantum-Enhanced Denoising Diffusion Model – Gerhard Stenzel
  • Dimensionality Reduction with Autoencoders for Efficient Classification with Variational Quantum Circuits – Jonas Maurer
  • Multi-Agent Exploration through Peer Incentivization – Johannes Tochtermann
  • Analyzing Reinforcement Learning strategies from a parameterized quantum walker – Lorena Wemmer
  • Quantum Multi-Agent Reinforcement Learning using Evolutionary Optimization – Felix Topp
  • Efficient quantum circuit architecture for parameterized coined quantum walks on many bipartite graphs – Viktoryia Patapovich
  • Scalable Discrete Communication in Decentralized MARL using Clustering – Valentin Kerle
  • Generalizing Agents in the Starcraft Multi-Agent Challenge – Balthasar Schüss
  • Quantum Enhanced Policy Gradient Methods for Reinforcement Learning – Mohamad Hgog
  • Embedding Classical Data for efficient Quantum Machine Learning – David Münzer
  • Efficient Data Embedding for offline Handwriting Recognition using Quantum Support Vector Machines – Leopold Bodendörfer
  • 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

👥 Community

  • IEEE/CVF Conference on Computer Vision and Pattern Recognition 2024: Reviewer
  • Springer Nature – Computer Science: Reviewer
  • IEEE International Conference on Quantum Software: Session Chair
  • IEEE International Conference on Quantum Computing and Engineering – Quantum Machine Learning Workshop: Program Committee
  • The 39th Annual AAAI Conference on Artificial Intelligence: Program Committee
  • Quantum Artificial Intelligence & Optimization 2025: Organizer & Session Chair

📚 Publications

Full List -> Google Scholar

2024

  • T. Rohe, S. Grätz, M. Kölle, S. Zielinski, J. Stein, and C. Linnhoff-Popien, „From Problem to Solution: A general Pipeline to Solve Optimisation Problems on Quantum Hardware,“ arXiv preprint arXiv:2406.19876, 2024.
    [BibTeX] 
    @article{rohe2024problem,
title={From Problem to Solution: A general Pipeline to Solve Optimisation Problems on Quantum Hardware},
author={Rohe, Tobias and Gr{\"a}tz, Simon and K{\"o}lle, Michael and Zielinski, Sebastian and Stein, Jonas and Linnhoff-Popien, Claudia},
journal={arXiv preprint arXiv:2406.19876},
year={2024}
}

  • M. Kölle, T. Witter, T. Rohe, G. Stenzel, P. Altmann, and T. Gabor, „A Study on Optimization Techniques for Variational Quantum Circuits in Reinforcement Learning,“ arXiv preprint arXiv:2405.12354, 2024.
    [BibTeX] 
    @article{kolle2024study,
title={A Study on Optimization Techniques for Variational Quantum Circuits in Reinforcement Learning},
author={K{\"o}lle, Michael and Witter, Timo and Rohe, Tobias and Stenzel, Gerhard and Altmann, Philipp and Gabor, Thomas},
journal={arXiv preprint arXiv:2405.12354},
year={2024}
}

  • L. Sünkel, M. Kölle, T. Rohe, and T. Gabor, „Towards Federated Learning on the Quantum Internet,“ in International Conference on Computational Science, 2024, p. 330–344.
    [BibTeX] 
    @inproceedings{sunkel2024towards,
title={Towards Federated Learning on the Quantum Internet},
author={S{\"u}nkel, Leo and K{\"o}lle, Michael and Rohe, Tobias and Gabor, Thomas},
booktitle={International Conference on Computational Science},
pages={330--344},
year={2024},
organization={Springer}
}

  • M. Zorn, P. Altmann, G. Stenzel, M. Kölle, C. Linnhoff-Popien, and T. Gabor, Self-Adaptive Robustness of Applied Neural-Network-Soups, 2024. doi:10.1162/isal_a_00811
    [BibTeX] [Download PDF] 
    @proceedings{zorn24selfadapt,
author = {Zorn, Maximilian and Altmann, Philipp and Stenzel, Gerhard and Kölle, Michael and Linnhoff-Popien, Claudia and Gabor, Thomas},
title = "{Self-Adaptive Robustness of Applied Neural-Network-Soups}",
volume = {ALIFE 2024: Proceedings of the 2024 Artificial Life Conference},
series = {Artificial Life Conference Proceedings},
pages = {74},
year = {2024},
month = {07},
doi = {10.1162/isal_a_00811},
url = {https://doi.org/10.1162/isal\_a\_00811},
eprint = {https://direct.mit.edu/isal/proceedings-pdf/isal2024/36/74/2461231/isal\_a\_00811.pdf},
}

  • S. Zielinski, J. Nüßlein, M. Kölle, T. Gabor, C. Linnhoff-Popien, and S. Feld, „Solving Max-3SAT Using QUBO Approximation,“ arXiv preprint arXiv:2409.15891, 2024.
    [BibTeX] 
    @article{zielinski2024solving,
title={Solving Max-3SAT Using QUBO Approximation},
author={Zielinski, Sebastian and N{\"u}{\ss}lein, Jonas and K{\"o}lle, Michael and Gabor, Thomas and Linnhoff-Popien, Claudia and Feld, Sebastian},
journal={arXiv preprint arXiv:2409.15891},
year={2024}
}

  • J. Stein, N. Roshani, M. Zorn, P. Altmann, M. Kölle, and C. Linnhoff-Popien, „Improving Parameter Training for VQEs by Sequential Hamiltonian Assembly,“ in Proceedings of the 16th International Conference on Agents and Artificial Intelligence – Volume 2: ICAART, 2024, pp. 99-109. doi:10.5220/0012312500003636
    [BibTeX] 
    @inproceedings{stein2023improving,
title={Improving Parameter Training for VQEs by Sequential Hamiltonian Assembly},
author={Stein, Jonas and Roshani, Navid and Zorn, Maximilian and Altmann, Philipp and K{\"o}lle, Michael and Linnhoff-Popien, Claudia},
booktitle={Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART},
year={2024},
pages={99-109},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012312500003636},
isbn={978-989-758-680-4},
issn={2184-433X},
}

  • J. Stein, T. Rohe, F. Nappi, J. Hager, D. Bucher, M. Zorn, M. Kölle, and C. Linnhoff-Popien, „Introducing Reducing-Width-QNNs, an AI-inspired Ansatz design pattern,“ in Proceedings of the 16th International Conference on Agents and Artificial Intelligence – Volume 3: ICAART, 2024, pp. 1127-1134. doi:10.5220/0012449800003636
    [BibTeX] 
    @inproceedings{stein2023introducing,
title={Introducing Reducing-Width-QNNs, an AI-inspired Ansatz design pattern},
author={Stein, Jonas and Rohe, Tobias and Nappi, Francesco and Hager, Julian and Bucher, David and Zorn, Maximilian and K{\"o}lle, Michael and Linnhoff-Popien, Claudia},
booktitle={Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART},
year={2024},
pages={1127-1134},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012449800003636},
isbn={978-989-758-680-4},
issn={2184-433X},
}

  • M. Kölle, M. Hgog, F. Ritz, P. Altmann, M. Zorn, J. Stein, and C. Linnhoff-Popien, „Quantum Advantage Actor-Critic for Reinforcement Learning,“ in Proceedings of the 16th International Conference on Agents and Artificial Intelligence – Volume 1: ICAART, 2024, pp. 297-304. doi:10.5220/0012383900003636
    [BibTeX] 
    @inproceedings{kolle2024quantum,
title={Quantum Advantage Actor-Critic for Reinforcement Learning},
author={K{\"o}lle, Michael and Hgog, Mohamad and Ritz, Fabian and Altmann, Philipp and Zorn, Maximilian and Stein, Jonas and Linnhoff-Popien, Claudia},
booktitle={Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART},
year={2024},
pages={297-304},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012383900003636},
isbn={978-989-758-680-4},
issn={2184-433X},
}

  • M. Kölle, T. Schubert, P. Altmann, M. Zorn, J. Stein, and C. Linnhoff-Popien, „A Reinforcement Learning Environment for Directed Quantum Circuit Synthesis,“ in Proceedings of the 16th International Conference on Agents and Artificial Intelligence – Volume 1: ICAART, 2024, pp. 83-94. doi:10.5220/0012383200003636
    [BibTeX] 
    @inproceedings{kolle2024reinforcement,
title={A Reinforcement Learning Environment for Directed Quantum Circuit Synthesis},
author={K{\"o}lle, Michael and Schubert, Tom and Altmann, Philipp and Zorn, Maximilian and Stein, Jonas and Linnhoff-Popien, Claudia},
booktitle={Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART},
year={2024},
pages={83-94},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012383200003636},
isbn={978-989-758-680-4},
issn={2184-433X},
}

2023

  • M. Kölle, A. Giovagnoli, J. Stein, M. B. Mansky, J. Hager, T. Rohe, R. Müller, and C. Linnhoff-Popien, „Weight Re-mapping for Variational Quantum Algorithms,“ in International Conference on Agents and Artificial Intelligence, 2023, p. 286–309.
    [BibTeX] 
    @inproceedings{kolle2023weight,
title={Weight Re-mapping for Variational Quantum Algorithms},
author={K{\"o}lle, Michael and Giovagnoli, Alessandro and Stein, Jonas and Mansky, Maximilian Balthasar and Hager, Julian and Rohe, Tobias and M{\"u}ller, Robert and Linnhoff-Popien, Claudia},
booktitle={International Conference on Agents and Artificial Intelligence},
pages={286--309},
year={2023},
organization={Springer}
}

  • T. Phan, F. Ritz, P. Altmann, M. Zorn, J. NNüßlein, M. Kölle, T. Gabor, and C. Linnhoff-Popien, „Attention-Based Recurrence for Multi-Agent Reinforcement Learning under Stochastic Partial Observability,“ in Proceedings of the 40th International Conference on Machine Learning (ICML), 2023.
    [BibTeX] [Download PDF] 
    @inproceedings{phanICML23,
author = {Thomy Phan and Fabian Ritz and Philipp Altmann and Maximilian Zorn and Jonas NN{\"u}{\ss}lein and Michael K{\"o}lle and Thomas Gabor and Claudia Linnhoff-Popien},
title = {Attention-Based Recurrence for Multi-Agent Reinforcement Learning under Stochastic Partial Observability},
year = {2023},
publisher = {PMLR},
booktitle = {Proceedings of the 40th International Conference on Machine Learning (ICML)},
location = {Hawaii, USA},
url  = {https://thomyphan.github.io/publication/2023-07-01-icml-phan},
eprint  = {https://thomyphan.github.io/files/2023-icml-preprint.pdf},
}

  • J. Stein, F. Chamanian, M. Zorn, J. Nüßlein, S. Zielinski, M. Kölle, and C. Linnhoff-Popien, „Evidence that PUBO outperforms QUBO when solving continuous optimization problems with the QAOA,“ , p. 2254–2262, 2023. doi:10.1145/3583133.3596358
    [BibTeX] [Download PDF] 
    @article{stein2023evidence,
title={Evidence that PUBO outperforms QUBO when solving continuous optimization problems with the QAOA},
author={Stein, Jonas and Chamanian, Farbod and Zorn, Maximilian and N{\"u}{\ss}lein, Jonas and Zielinski, Sebastian and K{\"o}lle, Michael and Linnhoff-Popien, Claudia},
year = {2023},
isbn = {9798400701207},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3583133.3596358},
doi = {10.1145/3583133.3596358},
booktitle = {Proceedings of the Companion Conference on Genetic and Evolutionary Computation},
pages = {2254–2262},
numpages = {9},
location = {Lisbon, Portugal},
series = {GECCO '23 Companion}
}

  • M. Kölle, S. Illium, M. Zorn, J. Nüßlein, P. Suchostawski, and C. Linnhoff-Popien, „Improving Primate Sounds Classification using Binary Presorting for Deep Learning.“ 2023.
    [BibTeX] 
    @inproceedings {koelle23primate,
title = {Improving Primate Sounds Classification using Binary Presorting for Deep Learning},
author = {K{\"o}lle, Michael and Illium, Steffen and Zorn, Maximilian and N{\"u}{\ss}lein, Jonas and Suchostawski, Patrick and Linnhoff-Popien, Claudia},
year = {2023},
organization = {Int. Conference on Deep Learning Theory and Application - DeLTA 2023},
publisher = {Springer CCIS Series},
}

2022

  • S. Illium, G. Griffin, M. Kölle, M. Zorn, J. Nüßlein, and C. Linnhoff-Popien, VoronoiPatches: Evaluating A New Data Augmentation MethodarXiv, 2022. doi:10.48550/ARXIV.2212.10054
    [BibTeX] [Download PDF] 
    @misc{https://doi.org/10.48550/arxiv.2212.10054,
doi = {10.48550/ARXIV.2212.10054},
url = {https://arxiv.org/abs/2212.10054},
author = {Illium, Steffen and Griffin, Gretchen and Kölle, Michael and Zorn, Maximilian and Nüßlein, Jonas and Linnhoff-Popien, Claudia},
keywords = {Computer Vision and Pattern Recognition (cs.CV), Machine Learning (cs.LG), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {VoronoiPatches: Evaluating A New Data Augmentation Method},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}

  • S. Illium, M. Zorn, C. Lenta, M. Kölle, C. Linnhoff-Popien, and T. Gabor, Constructing Organism Networks from Collaborative Self-ReplicatorsarXiv, 2022. doi:10.48550/ARXIV.2212.10078
    [BibTeX] [Download PDF] 
    @misc{https://doi.org/10.48550/arxiv.2212.10078,
doi = {10.48550/ARXIV.2212.10078},
url = {https://arxiv.org/abs/2212.10078},
author = {Illium, Steffen and Zorn, Maximilian and Lenta, Cristian and Kölle, Michael and Linnhoff-Popien, Claudia and Gabor, Thomas},
keywords = {Neural and Evolutionary Computing (cs.NE), Machine Learning (cs.LG), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Constructing Organism Networks from Collaborative Self-Replicators},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}

  • K. Schmid, M. Kölle, and T. Matheis, „Learning to Participate through Trading of Reward Shares,“ , 2022.
    [BibTeX] 
    @article{schmidlearning,
title = {Learning to Participate through Trading of Reward Shares},
author = {Schmid, Kyrill and Kölle, Michael and Matheis, Tim},
year = {2022}
}

  • M. Kölle, L. Rietdorf, and K. Schmid, Decentralized scheduling through an adaptive, trading-based multi-agent systemarXiv, 2022. doi:10.48550/ARXIV.2207.11172
    [BibTeX] [Download PDF] 
    @misc{https://doi.org/10.48550/arxiv.2207.11172,
doi = {10.48550/ARXIV.2207.11172},
url = {https://arxiv.org/abs/2207.11172},
author = {Kölle, Michael and Rietdorf, Lennart and Schmid, Kyrill},
keywords = {Artificial Intelligence (cs.AI), Machine Learning (cs.LG), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Decentralized scheduling through an adaptive, trading-based multi-agent system},
publisher = {arXiv},
year = {2022},
copyright = {arXiv.org perpetual, non-exclusive license}
}

  • 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
    [BibTeX] 
    @conference{multimod_icaart22,
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,},
year = {2022},
pages = {367-374},
publisher = {SciTePress},
organization = {INSTICC},
doi = {10.5220/0010898500003116},
isbn = {978-989-758-547-0}
}