47 citations · 88 across the 7 of their papers we have counts for
3 papers · 2 filters
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…
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…