8 citations · 9 across the 9 of their papers we have counts for
9 papers
The QuaST Decision Tree: Achieving Automation With Data-Based Recommendations
Benedikt Poggel, Lena Tokuhiro, Georg Kruse +1
Quantum computers are increasingly powerful. Software tools for the development of quantum-enhanced algorithms are maturing. However, the software stack still lacks the connection…
jNO: A JAX Library for Neural Operator and Foundation Model Training
Leon Armbruster, Rathan Ramesh, Georg Kruse +1
jNO (jax Neural Operators) is a JAX-native library for neural operators and foundation models with unified support for both data-driven and physics-informed training. Its core desi…
Physics-informed fine-tuning of foundation models for partial differential equations
Vlad Medvedev, Leon Armbruster, Christopher Straub +2
Foundation models for partial differential equations (PDEs) have emerged as powerful surrogates pre-trained on diverse physical systems, but adapting them to new downstream tasks r…
QAOA-Predictor: Forecasting Success Probabilities and Minimal Depths for Efficient Fixed-Parameter Optimization
Rodrigo Coelho, Georg Kruse, Jeanette Miriam Lorenz
Quantum Computing promises to solve complex combinatorial optimization problems more efficiently than classical methods, with the Quantum Approximate Optimization Algorithm (QAOA)…
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…
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…
Variational Quantum Circuit Design for Quantum Reinforcement Learning on Continuous Environments
Georg Kruse, Theodora-Augustina Dragan, Robert Wille +1
Quantum Reinforcement Learning (QRL) emerged as a branch of reinforcement learning (RL) that uses quantum submodules in the architecture of the algorithm. One branch of QRL focuses…
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,…