8 citations · 13 across the 5 of their papers we have counts for
5 papers
BenchQC -- Scalable and modular benchmarking of industrial quantum computing applications
Florian Geissler, Eric Stopfer, Christian Ufrecht +19
We present BenchQC, a research project funded by the state of Bavaria, which promotes an application-centric perspective for benchmarking real-world quantum applications. Diverse u…
Understanding the effects of data encoding on quantum-classical convolutional neural networks
Maureen Monnet, Nermine Chaabani, Theodora-Augustina Dragan +2
Quantum machine learning was recently applied to various applications and leads to results that are comparable or, in certain instances, superior to classical methods, in particula…
Quantum Multi-Agent Reinforcement Learning for Aerial Ad-hoc Networks
Theodora-Augustina Drăgan, Akshat Tandon, Carsten Strobel +2
Quantum machine learning (QML) as combination of quantum computing with machine learning (ML) is a promising direction to explore, in particular due to the advances in realizing qu…
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…
Quantum Reinforcement Learning for Solving a Stochastic Frozen Lake Environment and the Impact of Quantum Architecture Choices
Theodora-Augustina Drăgan, Maureen Monnet, Christian B. Mendl +1
Quantum reinforcement learning (QRL) models augment classical reinforcement learning schemes with quantum-enhanced kernels. Different proposals on how to construct such models empi…