3 papers
quant-ph2026
Reinforcement learning for ion shuttling on trapped-ion quantum computers
Maximilian Schier, Lea Richtmann, Christian Staufenbiel +4
Scalable trapped-ion quantum computing is commonly realized with modular chips that feature distinct zones with specific functionalities, such as storage, state preparation, and ga…
cs.CV2025
Cell Tracking according to Biological Needs -- Strong Mitosis-aware Multi-Hypothesis Tracker with Aleatoric Uncertainty
Timo Kaiser, Maximilian Schier, Bodo Rosenhahn
Cell tracking and segmentation assist biologists in extracting insights from large-scale microscopy time-lapse data. Driven by local accuracy metrics, current tracking approaches o…
cs.LG2025
Multi-Agent Reinforcement Learning for Inverse Design in Photonic Integrated Circuits
Yannik Mahlau, Maximilian Schier, Christoph Reinders +3
Inverse design of photonic integrated circuits (PICs) has traditionally relied on gradientbased optimization. However, this approach is prone to end up in local minima, which resul…