Sebastian Bugge Loeschcke

Sebastian Bugge Loeschcke is a PhD Fellow at the Machine Learning Section of the Department of Computer Science (DIKU), University of Copenhagen. His research spans theoretical and applied machine learning with focus on quantum machine learning, language modeling, and sustainability.

• Current affiliation: Machine Learning Section, DIKU
• Key research areas: Quantum-classical hybrid models, neural language processing, geospatial analysis
• Collaborative initiatives: SCIENCE AI Centre, TreeSense Centre

Loeschcke's recent work includes Coarse-To-Fine Tensor Trains for compact representations and LoQT: Low-Rank Adapters for Quantized Pretraining, reflecting his focus on efficient neural architectures and quantum-inspired methods. His publications address cross-disciplinary challenges in climate modeling, healthcare, and quantum computing.

Scientific contributions include:

• 2024: Tensor train compression methods for visual representations
• 2024: Low-rank adapter techniques for quantized models
• 2025: Quantum computing applications in molecular binding energy calculation
• 2025: Ethical frameworks for sustainable AI development
• 2025: Quantum dot array simulation tools (QDarts)

Loeschcke contributes to interdisciplinary projects involving:

• TreeSense (remote sensing of global tree resources)
• Quantum computing optimization with Danish research consortia