Research | Michael Kölle

Research

My research focuses on the intersection of Artificial Intelligence and Quantum Computing, exploring how quantum technologies can enhance machine learning methodologies. Specifically, I investigate the integration of quantum computing with AI to develop novel approaches within Quantum AI. By leveraging quantum algorithms and variational quantum circuits, my work aims to tackle complex optimization and learning problems that surpass the capabilities of classical computing, paving the way for more efficient and scalable intelligent systems.

Research Areas

My research areas include Quantum Artificial Intelligence, Automated Circuit Design, Reinforcement Learning, and Multi-Agent Systems. I aim to develop innovative solutions that leverage quantum computing to enhance AI methodologies and solve complex problems.

Optimizing Variational Quantum Circuits

Exploring strategies to improve training and performance of variational quantum circuits.

Quantum Circuit Synthesis and Architecture Search

Automated methods for designing and optimizing quantum circuits.

Quantum Kernel Methods and Anomaly Detection

Leveraging quantum kernels for anomaly detection in machine learning.

Quantum Reinforcement Learning

Leveraging quantum mechanics for reinforcement learning

Talks and Posters

I have presented my research at various conferences and workshops, sharing insights on Quantum AI, optimization techniques, and advancements in machine learning. My talks and posters highlight the potential of quantum technologies in transforming AI and solving real-world challenges.