4 papers
Multivariate quantum reservoir computing with discrete and continuous variable systems
Tobias Fellner, Jonas Merklinger, Christian Holm
Quantum reservoir computing is a promising paradigm for processing temporal data. So far, the primary focus has been on univariate time series. However, the most relevant and compl…
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…
Quantum vs. classical: A comprehensive benchmark study for predicting time series with variational quantum machine learning
Tobias Fellner, David Kreplin, Samuel Tovey +1
Variational quantum machine learning algorithms have been proposed as promising tools for time series prediction, with the potential to handle complex sequential data more effectiv…