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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…
Certifiably Robust Encoding Schemes
Aman Saxena, Tom Wollschläger, Nicola Franco +2
Quantum machine learning uses principles from quantum mechanics to process data, offering potential advances in speed and performance. However, previous work has shown that these m…
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…
Quadratic Advantage with Quantum Randomized Smoothing Applied to Time-Series Analysis
Nicola Franco, Marie Kempkes, Jakob Spiegelberg +1
As quantum machine learning continues to develop at a rapid pace, the importance of ensuring the robustness and efficiency of quantum algorithms cannot be overstated. Our research…