1 citations · 1 across the 7 of their papers we have counts for
8 papers
CleanQRL: Lightweight Single-file Implementations of Quantum Reinforcement Learning Algorithms
Georg Kruse, Rodrigo Coelho, Andreas Rosskopf +2
At the interception between quantum computing and machine learning, Quantum Reinforcement Learning (QRL) has emerged as a promising research field. Due to its novelty, a standardiz…
Physics-Informed PointNets for Modeling Electromagnetic Scattering from All-Dielectric Metasurfaces with Inclined Nanopillars
Leon Armbruster, Vlad Medvedev, Andreas Rosskopf
Metasurfaces are innovative planar optical structures capable of manipulating incident light properties. Accurate and computationally efficient modeling of such metasurfaces, parti…
Hard-constraining Neumann boundary conditions in physics-informed neural networks via Fourier feature embeddings
Christopher Straub, Philipp Brendel, Vlad Medvedev +1
We present a novel approach to hard-constrain Neumann boundary conditions in physics-informed neural networks (PINNs) using Fourier feature embeddings. Neumann boundary conditions…
Quantum-Efficient Kernel Target Alignment
Rodrigo Coelho, Georg Kruse, Andreas Rosskopf
In recent years, quantum computers have emerged as promising candidates for implementing kernels. Quantum Embedding Kernels embed data points into quantum states and calculate thei…
Hamiltonian-based Quantum Reinforcement Learning for Neural Combinatorial Optimization
Georg Kruse, Rodrigo Coehlo, Andreas Rosskopf +2
Advancements in Quantum Computing (QC) and Neural Combinatorial Optimization (NCO) represent promising steps in tackling complex computational challenges. On the one hand, Variatio…
Continuously Adapting Random Sampling (CARS) for Power Electronics Parameter Design
Dominik Happel, Philipp Brendel, Andreas Rosskopf +1
To date, power electronics parameter design tasks are usually tackled using detailed optimization approaches with detailed simulations or using brute force grid search grid search…
Restricted Global Optimization for QAOA
Peter Gleißner, Georg Kruse, Andreas Roßkopf
The Quantum Approximate Optimization Algorithm (QAOA) has emerged as a promising variational quantum algorithm for addressing NP hard combinatorial optimization problems. However,…
Parameter Optimization of LLC-Converter with multiple operation points using Reinforcement Learning
Georg Kruse, Dominik Happel, Stefan Ditze +2
The optimization of electrical circuits is a difficult and time-consuming process performed by experts, but also increasingly by sophisticated algorithms. In this paper, a reinforc…