984 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- University of StuttgartDE21 papers
- Fraunhofer Institute for Industrial EngineeringDE2 papers
- Fraunhofer Institute of Optronics, System Technologies and Image ExploitationDE2 papers
- Heilbronn UniversityDE2 papers
- Software (Germany)DE2 papers
- Stuttgart Media UniversityDE2 papers
- Carnegie Mellon UniversityUS1 paper
- Delft University of TechnologyNL1 paper
- Deutscher WetterdienstDE1 paper
- Esslingen University of Applied SciencesDE1 paper
- Forschungszentrum JülichDE1 paper
- Friedrich-Alexander-Universität Erlangen-NürnbergDE1 paper
9 papers · 1 filter
Data-Efficient Quantum Noise Modeling via Machine Learning
Yanjun Ji, Marco Roth, David A. Kreplin +2
Maximizing the computational utility of near-term quantum processors requires predictive noise models that inform robust, noise-aware compilation and error mitigation. Conventional…
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…
On the similarity of bandwidth-tuned quantum kernels and classical kernels
Roberto Flórez-Ablan, Marco Roth, Jan Schnabel
Quantum kernels (QK) are widely used in quantum machine learning applications; yet, their potential to surpass classical machine learning methods on classical datasets remains unce…
AutoQML: A Framework for Automated Quantum Machine Learning
Marco Roth, David A. Kreplin, Daniel Basilewitsch +7
Automated Machine Learning (AutoML) has significantly advanced the efficiency of ML-focused software development by automating hyperparameter optimization and pipeline construction…
Data Efficient Prediction of excited-state properties using Quantum Neural Networks
Manuel Hagelüken, Marco F. Huber, Marco Roth
Understanding the properties of excited states of complex molecules is crucial for many chemical and physical processes. Calculating these properties is often significantly more re…
Quantum Kernel Methods under Scrutiny: A Benchmarking Study
Jan Schnabel, Marco Roth
Since the entry of kernel theory in the field of quantum machine learning, quantum kernel methods (QKMs) have gained increasing attention with regard to both probing promising appl…
Reinforcement learning-based architecture search for quantum machine learning
Frederic Rapp, David A. Kreplin, Marco F. Huber +1
Quantum machine learning models use encoding circuits to map data into a quantum Hilbert space. While it is well known that the architecture of these circuits significantly influen…
Reduction of finite sampling noise in quantum neural networks
David A. Kreplin, Marco Roth
Quantum neural networks (QNNs) use parameterized quantum circuits with data-dependent inputs and generate outputs through the evaluation of expectation values. Calculating these ex…
Quantum Gaussian Process Regression for Bayesian Optimization
Frederic Rapp, Marco Roth
Gaussian process regression is a well-established Bayesian machine learning method. We propose a new approach to Gaussian process regression using quantum kernels based on paramete…