Publications

Studies:

  • S. Loos, F. Bickert, M. Dotzel, C. K. Tutschku, & S. Kaiser (2024). Potentials and Needs of the Quantum Computing Ecosystem. In O. Riedel, K. Hölzle, W. Bauer, & B. Bienzeisler (Eds.),
    url: https://doi.org/10.24406/publica-3632
  • 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

Scientific Publications

2026

2025

  • F. Bickert, M.J. Maier, S. Kaiser, K. Hölzle (2025). „Kollaborative Mensch-KI-Interaktion für datengestützte Vorausschau: Ein Human-in-the-Loop-Ansatz zum Trendmonitoring im Bereich Quantencomputing“, 18. Symposium für Vorausschau und Technologieplanung, Berlin.
    ISSN: 2195-5239
  • 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
  • Y. Ji, M. Roth, D. A. Kreplin, I. Polian, F. K. Wilhelm (2025). „Data-Efficient Quantum Noise Modeling via Machine Learning“,
    url: https://doi.org/10.48550/arXiv.2509.12933
  • L. Binkowski, G. Koßmann, C. Tutschku, and R. Schwonnek (2025). „Symmetry-based quantum algorithms for open-shop scheduling with hard constraints“. Academia Quantum, 2(3).
    url: https://doi.org/10.20935/AcadQuant7900
  • A. Sturm and N. Schillo (2025). „Efficient and Explicit Block Encoding of Finite Difference Discretizations of the Laplacian“.
    url: https://doi.org/10.48550/arXiv.2509.02429
  • L. Rullkötter, S. Weber, V. M. Katukuri, C. Tutschku, B. C. Mummaneni (2025). „Resource-efficient Variational Block-Encoding“.
    url: https://doi.org/10.48550/arXiv.2507.17658
  • M. Albus, M. Roth, J. Schnabel, M. Willmann (2025). „Application of quantum annealing for scalable robotic assembly line optimization: a case study“. In: Procedia CIRP (134), pp. 25–30.
    url: https://www.sciencedirect.com/science/article/pii/S2212827125004986
  • R. F. Ablan, M. Roth, J. Schnabel (2025). „On the similarity of bandwith-tuned quantum kernels and classcial kernels“. In: Quantum Sci. Technol. 10 035051.
    url: https://doi.org/10.1088/2058-9565/ade7ad
  • M. Hagelüken, M. F. Huber, and M. Roth (2025). „Data Efficient Prediction of excited-state properties using Quantum Neural Networks“. In: New Journal of Physics 27(054508)
    url: https://doi.org/10.1088/1367-2630/add203
  • 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, 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
  • J. Schnabel and M. Roth (2025). „Quantum Kernel Methods Under Scrutiny: A Benchmarking Study“. In: Quantum Machine Intelligence 7(58).
    url: https://doi.org/10.1007/s42484-025-00273-5

2024

2023

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
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