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] J. Berberich, D. Fink, D. Pranjić, C. Tutschku, C. Holm, »Training robust and generalizable quantum models«, 2023,

[2] D. Klau, M. Zöller, C. Tutschku, »Bringing Quantum Algorithms to Automated Machine Learning: A Systematic Review of AutoML Frameworks Regarding Extensibility for QML Algorithms«, 2023,

[3] H. Stühler, M.-A. Zöller, D. Klau, A. Beiderwellen-Bedrikow and C. Tutschku, »Benchmarking Automated Machine Learning Methods for Price Forecasting Applications«, 2023,

[4] F. Rapp and M. Roth, »Quantum Gaussian Process Regression for Bayesian Optimization«, 2023,

[5] 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,