19 citations · 58 across the 8 of their papers we have counts for
9 papers
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…
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…
Bringing Quantum Systems under Control: A Tutorial Invitation to Quantum Computing and Its Relation to Bilinear Control Systems
Julian Berberich, Robert L. Kosut, Thomas Schulte-Herbrüggen
Quantum computing comes with the potential to push computational boundaries in various domains including, e.g., cryptography, simulation, optimization, and machine learning. Exploi…
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…
Using quantum computers in control: interval matrix properties
Jan Schneider, Julian Berberich
Quantum computing provides a powerful framework for tackling computational problems that are classically intractable. The goal of this paper is to explore the use of quantum comput…
Training robust and generalizable quantum models
Julian Berberich, Daniel Fink, Daniel Pranjić +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…
Quantum computing through the lens of control: A tutorial introduction
Julian Berberich, Daniel Fink
Quantum computing is a fascinating interdisciplinary research field that promises to revolutionize computing by efficiently solving previously intractable problems. Recent years ha…
Robustness of quantum algorithms against coherent control errors
Julian Berberich, Daniel Fink, Christian Holm
Coherent control errors, for which ideal Hamiltonians are perturbed by unknown multiplicative noise terms, are a major obstacle for reliable quantum computing. In this paper, we pr…