18 citations · 52 across the 12 of their papers we have counts for
13 papers
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…
Discrete Randomized Smoothing Meets Quantum Computing
Tom Wollschläger, Aman Saxena, Nicola Franco +2
Breakthroughs in machine learning (ML) and advances in quantum computing (QC) drive the interdisciplinary field of quantum machine learning to new levels. However, due to the susce…
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…
Building Continuous Quantum-Classical Bayesian Neural Networks for a Classical Clinical Dataset
Alona Sakhnenko, Julian Sikora, Jeanette Miriam Lorenz
In this work, we are introducing a Quantum-Classical Bayesian Neural Network (QCBNN) that is capable to perform uncertainty-aware classification of classical medical dataset. This…
Constructing Optimal Noise Channels for Enhanced Robustness in Quantum Machine Learning
David Winderl, Nicola Franco, Jeanette Miriam Lorenz
With the rapid advancement of Quantum Machine Learning (QML), the critical need to enhance security measures against adversarial attacks and protect QML models becomes increasingly…
Benchmarking Quantum Generative Learning: A Study on Scalability and Noise Resilience using QUARK
Florian J. Kiwit, Maximilian A. Wolf, Marwa Marso +4
Quantum computing promises a disruptive impact on machine learning algorithms, taking advantage of the exponentially large Hilbert space available. However, it is not clear how to…
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…
Quantum Neural Networks under Depolarization Noise: Exploring White-Box Attacks and Defenses
David Winderl, Nicola Franco, Jeanette Miriam Lorenz
Leveraging the unique properties of quantum mechanics, Quantum Machine Learning (QML) promises computational breakthroughs and enriched perspectives where traditional systems reach…
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…