2026
- L. Pastori, A. Grundner, V. Eyring, M. Schwabe: Quantum neural networks for cloud cover parameterizations in climate models. Machine Learning: Earth 2, 015008 (2026)
(see News for annoucements of preprints)
2025
- V. Sarandrea: Master Thesis LMU Munich – Explainable Quantum Machine Learning for cloud cover parametrization
- M. Schwabe et al.: Opportunities and challenges of quantum computing for climate modelling, Environmental Data Science 4, e35 (2025). https://doi.org/10.1017/eds.2025.10010
- P. Bonnet et al: Tuning the ICON-A 2.6. 4 climate model with machine-learning-based emulators and history matching, Geoscientific Model Development 18 (12), 3681-3706. https://doi.org/10.5194/gmd-18-3681-2025
- E. Sarauer et al.: A physics-informed machine learning parameterization for cloud microphysics in ICON. Environmental Data Science 4, e40 (2025). https://doi.org/10.1017/eds.2025.10016
- Quantum Solution for Nonlinear Differential Equations: Carleman and Liouville Linearization
- A. Häbel, N. Klement, V. Eyring, M. Schwabe: Quantum Solution for Nonlinear Differential Equations: Carleman and Liouville Linearization. 2025 IEEE International Conference on Quantum Artificial Intelligence (QAI), pp. 147-152 (2025), doi: 10.1109/QAI63978.2025.00030.
- M. Schwabe, L. Pastori, V. Sarandrea and V. Eyring: Quantum Machine Learning for Climate Modelling, 2025 IEEE International Conference on Quantum Artificial Intelligence (QAI), Naples, Italy, pp. 73-78 (2025), doi: 10.1109/QAI63978.2025.00019.
Poster at the DLR Quantum Computing Austauschforum 5/2023 (Language: German)
