2 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.LG2025
Model-free reinforcement learning with noisy actions for automated experimental control in optics
Lea Richtmann, Viktoria-S. Schmiesing, Dennis Wilken +5
Setting up and controlling optical systems is often a challenging and tedious task. The high number of degrees of freedom to control mirrors, lenses, or phases of light makes autom…