5 papers · 1 filter
Robustness of quantum algorithms: Worst-case fidelity bounds and implications for design
Julian Berberich, Tobias Fellner, Robert L. Kosut +1
Errors occurring on noisy hardware pose a key challenge to reliable quantum computing. Existing techniques such as error correction, mitigation, or suppression typically separate t…
Training robust and generalizable quantum models
Julian Berberich, Daniel Fink, Daniel PranjiÄ +2
Adversarial robustness and generalization are both crucial properties of reliable machine learning models. In this paper, we study these properties in the context of quantum machin…
The interplay of robustness and generalization in quantum machine learning
Julian Berberich, Tobias Fellner, Christian Holm
While adversarial robustness and generalization have individually received substantial attention in the recent literature on quantum machine learning, their interplay is much less…
Robustness and Generalization in Quantum Reinforcement Learning via Lipschitz Regularization
Nico Meyer, Julian Berberich, Christopher Mutschler +1
Quantum machine learning leverages quantum computing to enhance accuracy and reduce model complexity compared to classical approaches, promising significant advancements in various…
Robustness of optimal quantum annealing protocols
Niklas Funcke, Julian Berberich
Noise in quantum computing devices poses a key challenge in their realization. In this paper, we study the robustness of optimal quantum annealing protocols against coherent contro…