Michael Kastoryano

Michael Kastoryano is an Associate Professor in the Machine Learning group at the University of Copenhagen (DI KU). His research focuses on quantum computing, quantum algorithms, and their intersections with machine learning and theoretical physics. He explores topics such as quantum-inspired differential equation solvers, tensor network simulations, and quantum thermal state preparation. His work also delves into quantum error correction, quantum chemistry simulations, and the expressive power of neural network models for quantum systems.

Key research interests include quantum algorithms for complex systems, quantum Gibbs samplers, and the development of efficient computational methods for quantum phenomena. His recent publications highlight advancements in low-rank adapters for quantized pretraining, coarse-to-fine tensor representations, and the evaluation of quantum advantage in computational chemistry.

Dr. Kastoryano's contributions span theoretical frameworks and practical implementations, with a particular emphasis on bridging quantum computing and machine learning. His articles reflect a deep engagement with both foundational quantum mechanics and applied computational challenges.

No scientific awards are listed. His work has been supported through collaborations within the University of Copenhagen's research infrastructure, focusing on machine learning and quantum technologies.