18 papers
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…
Benchmarking M-LTSF: Frequency and Noise-Based Evaluation of Multivariate Long Time Series Forecasting Models
Nick Janssen, Melanie Schaller, Bodo Rosenhahn
Understanding the robustness of deep learning models for multivariate long-term time series forecasting (M-LTSF) remains challenging, as evaluations typically rely on real-world da…
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…
Naga: Vedic Encoding for Deep State Space Models
Melanie Schaller, Nick Janssen, Bodo Rosenhahn
This paper presents Naga, a deep State Space Model (SSM) encoding approach inspired by structural concepts from Vedic mathematics. The proposed method introduces a bidirectional re…