7 papers
Is data-efficient learning feasible with quantum models?
Alona Sakhnenko, Christian B. Mendl, Jeanette M. Lorenz
The importance of analyzing nontrivial datasets when testing quantum machine learning (QML) models is becoming increasingly prominent in literature, yet a cohesive framework for un…
Generalization Bounds in Hybrid Quantum-Classical Machine Learning Models
Tongyan Wu, Amine Bentellis, Alona Sakhnenko +1
Hybrid classical-quantum models aim to harness the strengths of both quantum computing and classical machine learning, but their practical potential remains poorly understood. In t…
Enhancing the Scalability of Classical Surrogates for Real-World Quantum Machine Learning Applications
Philip Anton Hernicht, Alona Sakhnenko, Corey O'Meara +2
Quantum machine learning (QML) presents potential for early industrial adoption, yet limited access to quantum hardware remains a significant bottleneck for deployment of QML solut…
Identifying Bottlenecks of NISQ-friendly HHL algorithms
Marc Andreu Marfany, Alona Sakhnenko, Jeanette Miriam Lorenz
Quantum computing promises enabling solving large problem instances, e.g. large linear equation systems with HHL algorithm, once the hardware stack matures. For the foreseeable fut…
A Hyperparameter Study for Quantum Kernel Methods
Sebastian Egginger, Alona Sakhnenko, Jeanette Miriam Lorenz
Quantum kernel methods are a promising method in quantum machine learning thanks to the guarantees connected to them. Their accessibility for analytic considerations also opens up…
Predominant Aspects on Security for Quantum Machine Learning: Literature Review
Nicola Franco, Alona Sakhnenko, Leon Stolpmann +4
Quantum Machine Learning (QML) has emerged as a promising intersection of quantum computing and classical machine learning, anticipated to drive breakthroughs in computational task…