Beiträge in Tagungsbänden

De Maio, V., Kanatbekova, M., Zilk, F., Friis, N., Guggemos, T., & Brandic, I. (2024). Training Computer Scientists for the Challenges of Hybrid Quantum-Classical Computing. In 2024 IEEE 24th International Symposium on Cluster, Cloud and Internet Computing (CCGrid) (pp. 626–635). https://doi.org/10.1109/CCGrid59990.2024.00075 ( reposiTUm)
Herbst, S., De Maio, V., & Brandic, I. (2024). Streaming IoT Data and the Quantum Edge: A Classic/Quantum Machine Learning Use Case. In Euro-Par 2023: Parallel Processing Workshops : Euro-Par 2023 International Workshops Limassol, Cyprus, August 28 – September 1, 2023 Revised Selected Papers, Part I (pp. 177–188). Springer. https://doi.org/10.1007/978-3-031-50684-0_14 ( reposiTUm)

Preprints

De Maio, V., Kanatbekova, M., Zilk, F., Friis, N., Guggemos, T., & Brandic, I. (2024). Training Computer Scientists for the Challenges of Hybrid Quantum-Classical Computing. arXiv. https://doi.org/10.48550/arXiv.2403.00885 ( reposiTUm)
Herbst, S., De Maio, V., & Brandic, I. (2024). On Optimizing Hyperparameters for Quantum Neural Networks. arXiv. https://doi.org/10.48550/arXiv.2403.18579 ( reposiTUm)
Cranganore, S. S., De Maio, V., Brandic, I., & Deelman, E. (2024). Paving the Way to Hybrid Quantum-Classical Scientific Workflows. arXiv. https://doi.org/10.48550/arXiv.2404.10389 ( reposiTUm)