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quant-ph2026

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…

quant-ph2026

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)…

quant-ph2025

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…

quant-ph2025

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…

quant-ph2024

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…