The findings obtained in the AutoQML research project will be prepared for the scientific community in a series of publications as key research results. 

Scientific publications 

[1] H. Stühler, D. Pranjić and Christian Tutschku "Evaluating Quantum Support Vector Regression Methods for Price Forecasting Applications", 2024,

[2] D. Klau, H. Krause,  D. A. Kreplin, M. Roth, C. Tutschku and M. Zöller "AutoQML – A Framework for Automated Quantum Machine Learning", 2023,

[3] D. A. Kreplin, M. Willmann, J. Schnabel, F. Rapp and M. Roth "sQUlearn – A Python Library for Quantum Machine Learning", 2023,
[4] J. Berberich, D. Fink, D. Pranjić, C. Tutschku and C. Holm, "Training robust and generalizable quantum models", 2023,
[5] D. Klau, M. Zöller and C. Tutschku, "Bringing Quantum Algorithms to Automated Machine Learning: A Systematic Review of AutoML Frameworks Regarding Extensibility for QML Algorithms", 2023,
[6] H. Stühler, M.-A. Zöller, D. Klau, A. Beiderwellen-Bedrikow and C. Tutschku, "Benchmarking Automated Machine Learning Methods for Price Forecasting Applications", 2023,
[7] F. Rapp and M. Roth, "Quantum Gaussian Process Regression for Bayesian Optimization", 2023,
[8] P.-A. Matt, R. Ziegler, D. Brajovic, M. Roth and M. F. Huber, "A Nested Genetic Algorithm for Explaining Classification Data Sets with Decision Rules", 2022,