3 papers
quant-ph2025
Introducing the Kernel Descent Optimizer for Variational Quantum Algorithms
Lars Simon, Holger Eble, Manuel Radons
In recent years, variational quantum algorithms have garnered significant attention as a candidate approach for near-term quantum advantage using noisy intermediate-scale quantum (…
quant-ph2025
Quantum reinforcement learning in dynamic environments
Oliver Sefrin, Manuel Radons, Lars Simon +1
Combining quantum computing techniques in the form of amplitude amplification with classical reinforcement learning has led to the so-called "hybrid agent for quantum-accessible re…
quant-ph2024
Unsupervised Quantum Anomaly Detection on Noisy Quantum Processors
Daniel PranjiÄ, Florian Knäble, Philipp Kunst +6
Whether in fundamental physics, cybersecurity or finance, the detection of anomalies with machine learning techniques is a highly relevant and active field of research, as it poten…