2025

• R. F. Ablan, M. Roth, J. Schnabel (2025). „On the similarity of bandwith-tuned quantum kernels and classcial kernels“.
  url: https://doi.org/10.48550/arXiv.2503.05602
• D. A. Kreplin, M. Willmann, J. Schnabel, F. Rapp, M. Hagelüken, M. Roth (2025). “sQUlearn: A Python Library for Quantum Machine Learning”. In: IEEE Software 01, pp. 1–6.
  url: https://doi.ieeecomputersociety.org/10.1109/MS.2025.3527736.
• S. S. Ram, M. Molli, V. M. Katukuri, B. C. Mummaneni (2025). “Optimizing Superconducting Qubit Performance: A Theoretical Framework for Design, Analysis, and Calibration”.
  url: https://doi.org/10.48550/arXiv.2501.17825.
• F. Rapp, D. A. Kreplin, M. F. Huber, M. Roth (2025). “Reinforcement learning-based architecture search for quantum machine learning”. In: Mach. Learn.: Sci. Technol.
  url: https://doi.org/10.1088/2632-2153/adaf75.
• M. Roth, D. A. Kreplin, D. Basilewitsch, J. F. Bravo, D. Klau, M. Marinov, D. Pranjić, H. Stühler, M. Willmann, M. Zöller (2025). „AutoQML: A Framework for Automated Machine Learning“.
  url: https://doi.org/10.48550/arXiv.2502.21025

2024

• D. Basilewitsch, J. F. Bravo, C. Tutschku, F. Struckmeier (2024). “Quantum Neural Networks in Practice: A Comparative Study with Classical Models from Standard Data Sets to Industrial Images”.
  url: https://doi.org/10.48550/arXiv.2411.19276
• M. Hagelüken, M. F. Huber, and M. Roth (2024). Data Efficient Prediction of excited-state properties using Quantum Neural Networks.
  url: https://doi.org/10.48550/arXiv.2412.09423.
• D. A. Kreplin and M. Roth (2024). “Reduction of finite sampling noise in quantum neural networks”. In: Quantum 8, p. 1385.
  url: https://doi.org/10.22331/q-2024-06-25-1385.
• P.-A. Matt and M. Roth (2024). A heuristic for solving the irregular strip packing problem with quantum optimization.
  url: https://doi.org/10.48550/arXiv.2402.17542.
• B. C. Mummaneni, S. Chen, W. Hübner, G. Lefkidis (2024). “Investigation of the exact spin channels in laser-induced spin dynamics in two mononuclear Cu(ii) complexes”.
  url: https://doi.org/10.1039/D4CP01086H.
• D. Pranjić, F. Knäble, P. Kunst, D. Kutzias, D. Klau, C. Tutschku, L. Simon, M. Kraus, A. Abedi (2024). “Unsupervised Quantum Anomaly Detection on Noisy Quantum Processors”.
  url: https://doi.org/10.48550/arXiv.2411.16970.
• D. Pranjić, B. C. Mummaneni, C. Tutschku (2024). “Quantum Annealing based Feature Selection in Machine Learning”.
  url: https://doi.org/10.48550/arXiv.2411.19609.
• F. Rapp and M. Roth (2024). “Quantum Gaussian process regression for Bayesian optimization”. In: Quantum Machine Intelligence 6.5 (1).
  url: https://doi.org/10.1007/s42484-023-00138-9.
• N. Schillo (2024). “Quantum Algorithms and Quantum Machine Learning for Differential Equations”.
  url: http://dx.doi.org/10.18419/opus-13866.
• N. Schillo and A. Sturm (2024). “Quantum Circuit Learning on NISQ Hardware”.
  url: https://doi.org/10.48550/arXiv.2405.02069.
• J. Schnabel and M. Roth (2024). Quantum Kernel Methods under Scrutiny: A Benchmarking Study.
  url: https://doi.org/10.48550/arXiv.2409.04406.
• A. Sturm, B. C. Mummaneni, L. Rullkötter (2024). “Unlocking Quantum Optimization: A Use Case Study on NISQ Systems”.
  url: https://doi.org/10.48550/arXiv.2404.07171
• H. Stühler, D. Klau, M.-A. Zöller, A. Beiderwellen-Bedrikow, C. Tutschku (2024). “End-to-End Implementation of Automated Price Forecasting Applications”. In: SN Computer Science 5(402).
  url: https://doi.org/10.1007/s42979-024-02735-2
• H. Stühler, D. Pranjić, Christian Tutschku (2024). “Evaluating Quantum Support Vector Regression Methods for Price Forecasting Applications”. In Proceedings of the 16th International Conference on Agents and Artificial Intelligence – Volume 3: ICAART.
  url: https://doi.org/10.5220/0012351400003636.
• M. Willmann, M. Albus, J. Schnabel, M. Roth (2024). “Application of quantum annealing for scalable robotic assembly line optimization: a case study”. Accepted at CIRP CMS 2025 and for publication in Procedia CIRP.
  url: https://doi.org/10.48550/arXiv.2412.09239.

2023

• J. Berberich, D. Fink, D. Pranjić, C. Tutschku, C. Holm (2023). “Training robust and generalizable quantum models”.
  url: https://doi.org/10.48550/arXiv.2311.11871
• D. Klau, H. Krause, D. A. Kreplin, M. Roth, C. Tutschku, M. Zöller (2023). “AutoQML – A Framework for Automated Quantum Machine Learning”.
  url: https://www.digital.iao.fraunhofer.de/content/dam/iao/ikt/de/documents/AutoQML_Framework.pdf
• D. Klau, M. Zöller, C. Tutschku (2023). “Bringing Quantum Algorithms to Automated Machine Learning: A Systematic Review of AutoML Frameworks Regarding Extensibility for QML Algorithms”.
  url: https://doi.org/10.48550/arXiv.2310.04238.
• G. Koßmann, L. Binkowski, C. Tutschku, R. Schwonnek (2023). “Open-Shop Scheduling with Hard Constraints”.
  url: https://doi.org/10.48550/arXiv.2211.05822.
• H. Stühler, M.-A. Zöller, D. Klau, A. Beiderwellen-Bedrikow, C. Tutschku (2023). “Benchmarking Automated Machine Learning Methods for Price Forecasting Applications”.
  url: https://doi.org/10.5220/0012051400003541.
• A. Sturm (2023). “Theory and Implementation of the Quantum Approximate Optimization Algorithm: A Comprehensible Introduction and Case Study Using Qiskit and IBM Quantum Computers”.
  url: https://doi.org/10.48550/arXiv.2301.09535.
• C. Tutschku, A. Sturm, F. Knäble, B. C. Mummaneni, D. Pranjic, C. Stephan, D. B. Mayer, G. Koßmann, M. Roth, P.-A. Matt, A. Grigorjan, T. Wellens, K. König, M. Beisel, F. Truger, F. Shagieva, O. Denninger, S. Garhofer (2023). “Quantencomputing in der industriellen Applikation. Vom Algorithmen-, Markt- und Hardwareüberblick hin zu anwendungszentriertem Quantensoftware-Engineering“.
  url: https://doi.org/10.24406/publica-805.

2022

• P.-A. Matt, R. Ziegler, D. Barjovic, M. Roth, M. F. Huber (2022), “A Nested Genetic Algorithm for Explaining Classification Data Sets with Decision Rules”. url:  https://doi.org/10.48550/arXiv.2209.07575