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• L. Rullkötter, S. Weber, V. M. Katukuri, C. Tutschku, B. C. Mummaneni (2025). „Resource-efficient Variational Block-Encoding“.
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• 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.
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• 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.
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• 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)
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• 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.
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• S. S. Ram, M. Molli, V. M. Katukuri, B. C. Mummaneni (2025). „Optimizing Superconducting Qubit Performance: A Theoretical Framework for Design, Analysis, and Calibration”.
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• 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.
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• 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“.
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• J. Schnabel and M. Roth (2025). „Quantum Kernel Methods Under Scrutiny: A Benchmarking Study“. In: Quantum Machine Intelligence 7(58).
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• 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”.
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• D. A. Kreplin and M. Roth (2024). “Reduction of finite sampling noise in quantum neural networks”. In: Quantum 8, p. 1385.
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• P.-A. Matt and M. Roth (2024). A heuristic for solving the irregular strip packing problem with quantum optimization.
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• 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”.
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• 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”.
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• D. Pranjić, B. C. Mummaneni, C. Tutschku (2024). “Quantum Annealing based Feature Selection in Machine Learning”.
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• F. Rapp and M. Roth (2024). “Quantum Gaussian process regression for Bayesian optimization”. In: Quantum Machine Intelligence 6.5 (1).
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• N. Schillo (2024). “Quantum Algorithms and Quantum Machine Learning for Differential Equations”.
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• A. Sturm, B. C. Mummaneni, L. Rullkötter (2024). “Unlocking Quantum Optimization: A Use Case Study on NISQ Systems”.
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• 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).
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• 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.
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• J. Berberich, D. Fink, D. Pranjić, C. Tutschku, C. Holm (2023). “Training robust and generalizable quantum models”.
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• D. Klau, H. Krause, D. A. Kreplin, M. Roth, C. Tutschku, M. Zöller (2023). “AutoQML – A Framework for Automated Quantum Machine Learning”.
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• 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”.
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• G. Koßmann, L. Binkowski, C. Tutschku, R. Schwonnek (2023). “Open-Shop Scheduling with Hard Constraints”.
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• H. Stühler, M.-A. Zöller, D. Klau, A. Beiderwellen-Bedrikow, C. Tutschku (2023). “Benchmarking Automated Machine Learning Methods for Price Forecasting Applications”.
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• A. Sturm (2023). “Theory and Implementation of the Quantum Approximate Optimization Algorithm: A Comprehensible Introduction and Case Study Using Qiskit and IBM Quantum Computers”.
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• 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