Die im Rahmen des AutoQML-Forschungsprojekts gewonnenen Erkenntnisse wurden im Lauf des Projekts in einer Reihe von Publikationen als zentrale Forschungsergebnisse für die wissenschaftliche Fachwelt aufbereitet.
- D. Basilewitsch, J. F. Bravo, C. Tutschku, F. Struckmeier (2025). „Quantum neural networks in practice: a comparative study with classical models from standard data sets to industrial images“. In Quantum Mach. Intell. 7, 110.
url: https://doi.org/10.1007/s42484-025-00336-7 - D. Pranjić, B. C. Mummaneni, C. Tutschku (2025). “Quantum Annealing based Feature Selection”. In: Neurocomputing,
url: https://doi.org/10.1016/j.neucom.2025.131673. - 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. - M. Roth, D. A. Kreplin, D. Basilewitsch, J. F. Bravo, D. Klau, M. Marinov, D. Pranjić, P. Schichtel, H. Stuehler, M. Willmann, M. Zoeller (2025). „AutoQML: A Framework for Automated Machine Learning,“ in 2025 IEEE International Conference on Quantum Software (QSW), Helsinki, Finland, 2025, pp. 81-91,
url: https://doi.ieeecomputersociety.org/10.1109/QSW67625.2025.00019 - H. Stühler and D. Pranjic (2025) „Quanten-maschinelle Lernmethoden in der Preisprognose von gebrauchten Baumaschinen“. In Zeitschrift für wirtschaftlichen Fabrikbetrieb, vol. 120, no. 5, pp. 352-357.
url: https://doi.org/10.1515/zwf-2024-0163 - 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. - 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. - 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. - 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. - 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. - 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. - F. Rapp and M. Roth (2023). „Quantum Gaussian Process Regression for Bayesian Optimization“.
url: https://doi.org/10.48550/arXiv.2304.12923.