Michael Kölle, M.Sc.

• No open thesis topics

If you are interested in one of the topics, send us a Thesis Inquiry.

• Assessing Adversarial Robustness of Variational Quantum Circuits in Reinforcement Learning - Sulwan Alomari
• Privacy-Preserving Training of Quantum Circuits via Homomorphic Encryption - Sebastian Wölckert

• Learning Independent Multi-Agent Flocking Behavior With Reinforcement Learning - Gregor Reischl
• Exploring the Practical Application of Quantum-Native Self-Attention for Quantum Vision Transformers - Joel Furtak
• Masked Encoder with Projection Layer for Anomaly Detection – Anna Simon
• Offline Quantum Reinforcement Learning using Metaheuristic Optimization Strategies – Frederick Bickel
• Assessing Noise Robustness of Variational Quantum Circuits in Reinforcement Learning Environments - Justin Klein
• Exploring Second-Order Predator-Prey Marine Ecosystem Balance with MARL – Tim-Luis Hartenfels
• Quantum Transformers: Leveraging Variational Quantum Circuits for Natural Language Processing – Julian Hager
• Quantum Architecture Search for Solving Quantum Machine Learning Tasks – Simon Salfer
• Analysis and Improvement of Retrieval Quality in the Context of RAG using LLMs on the Code Domain – Melvin Tjiok
• Minimizing Teleportation and Enhancing Fidelity in Distributed Quantum Computing using a Multi-Objective Evolutionary Algorithm – Abasin Omerzai
• Emergent Cooperation in Quantum Multi-Agent Reinforcement Learning Using Communication – Christian Reff
• Emerging Cooperation through Quantum Entanglement in Multi-Agent Systems – Marvin Heinrich
• 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
• QUBO Generation for (MAX-)3SAT Using Generative AI Methods – Philippe Wehr
• Evaluating Parameter-Based Training Performance of Neural Network and Variational Quantum Circuits – Alexander Feist
• Exploring Second-Order Predator-Prey Marine Ecosystem Balance with MARL – Tim-Luis Hartenfels
• 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
• Quantum Reinforcement Learning via Parameterized Quantum Walks – Sabrina Egger
• Evaluating Mutation Techniques in Genetic Algorithm-Based Quantum Circuit Synthesis – Tom Bintener
• Exploring QuGANs for Realistic Graph Generation – An Exploratory Study – Florian Burger
• 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

• Rechnerarchitektur: SS25, 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: WS24/25, WS23/24, WS22/2

• Quantum Computing Programming: WS24/25, SS24, WS23/24, SS23, WS22/23, SS22, WS21/22, SS21

• Trends in Mobilen und Verteilten Systemen Seminar: WS25/26, WS24/25, SS24, WS23/24, SS23, WS22/23, SS22, WS21/22, SS21
• AI for Health Masterseminar: SS25

• Arbeitsgemeinschaft AI for Health: WS25/26, SS25