2 citations · 5 across the 20 of their papers we have counts for
5 papers · 1 filter
FLEET: Token-Based Feature Extraction for Event Camera-based Reinforcement Learning
Tristan Gottwald, Maximilian Schier, Melanie Schaller +1
Event cameras generate asynchronous, high-frequency data streams offering spatially sparse information at lower latency than traditional cameras. In principle, these properties sho…
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…
Numerical field optimization for enhanced efficiency in time-reversible gradient computation of open-source GPU-accelerated FDTD simulations
Yannik Mahlau, Lukas Berg, Bodo Rosenhahn
Finite-difference time-domain (FDTD) simulations often involve physical quantities spanning multiple orders of magnitude, such as the speed of light or electromagnetic field amplit…
Gradient-Informed Bayesian and Interior Point Optimization for Efficient Inverse Design in Nanophotonics
Yannik Mahlau, Yannick Augenstein, Tyler W. Hughes +2
Inverse design, particularly geometric shape optimization, provides a systematic approach for developing high-performance nanophotonic devices. While numerous optimization algorith…
Stochastic Neural Networks for Quantum Devices
Bodo Rosenhahn, Tobias J. Osborne, Christoph Hirche
This work presents a formulation to express and optimize stochastic neural networks as quantum circuits in gate-based quantum computing. Motivated by a classical perceptron, stocha…