Short Bio

Sabrina is a PhD student and project assistant at the HPC research group at TU Wien. She is supervised by Prof. Ivona Brandic and Prof. Sabine Andergassen as part of the CAIML doctoral school. A data scientist by training, her research is focused on Quantum Machine Learning, i.e., she is working on Machine Learning algorithms for quantum computers and the integration of quantum computers into data centers.

Sabrina holds a BSc degree (2021) in Business Informatics and a Dipl.-Ing. (equivalent to MSc, 2023) in Data Science, with specialization in Machine Learning and High Performance Computing, from TU Wien. Since 2024, she is pursuing a PhD. She has won several scholarships and fellowships, most notably the ETH Zürich Summer Research Fellowship (2023), where she spent 2 months in Prof. Thomas Hofmann’s group at ETH Zürich, researching the efficiency of Large Language models. Beyond that, she has won the Siemens Award of Excellence (2023), the Huawei Seeds for the Future Scholarship (2021), and merit-based scholarships of TU Wien (2019 & 2021).

She has publications at top-tier venues in Machine Learning and Quantum Computing, including ICLR 2025 and QCE 2024.

PhD Project - Learning Paradigms for Quantum Computers

Supervised by Ivona Brandic and Sabine Andergassen

Sabrina’s research focuses on developing learning paradigms for quantum computers. Starting with a benchmarking project of classical and quantum ML as her master thesis, she is currently focusing on the theory of QML models, in particular, characterization of individual components. Due to trainability issues in models that can already be executed on today’s hardware, she also has a strong interest in scalable QML models that can be run on larger machines as well.

Publications and Conferences

Conference Proceedings

• Herbst, S., Cranganore, S.S., De Maio, V., Brandić, I. (2024). “Exploring Channel Distinguishability in Local Neighborhoods of the Model Space in Quantum Neural Networks”. In: The Thirteenth International Conference on Learning Representations (ICLR) 2025, Singapore, Apr 24-28, 2025. https://openreview.net/forum?id=gDcL7cgZBt
• Herbst, S., De Maio, V., Brandić, I. (2024). “On Optimizing Hyperparameters for Quantum Neural Networks”. In: 2024 IEEE International Conference on Quantum Computing and Engineering (QCE), Montreal, QC, Canada, 2024, pp. 1478-1489. doi: 10.1109/QCE60285.2024.00174.
• Herbst, S., De Maio, V., Brandic, I. (2024). “Streaming IoT Data and the Quantum Edge: A Classic/Quantum Machine Learning Use Case”. In: Zeinalipour, D., et al. Euro-Par 2023: Parallel Processing Workshops. Euro-Par 2023. Lecture Notes in Computer Science, vol 14351. Springer, Cham. doi: 10.1007/978-3-031-50684-0_14. Limassol, Cyprus.

Reviews

Sabrina has served as a PC Member in the artifact evaluation track of CCGrid 2025. Beyond that, she has reviewed for HPDC 2024 and QCE 2024.

Presentations

• On Optimizing Hyperparameters for Quantum Neural Networks. IEEE International Conference on Quantum Computing and Engineering (QCE). 2024. Montreal, Canada.
• AI for Communication in Hybrid Classic Quantum Systems. CAIML Symposium. 2024. Vienna, Austria.
• On Hyperparameter Optimization in Quantum Neural Networks. Quantum Optics and Spectroscopy. Institute for Experimental Physics. Universität Innsbruck. 2024. Innsbruck, Austria.
• Beyond 0’s and 1’s ‑ Exploring the Complexities of Noise, Data Encoding, and Hyperparameter Optimization in Quantum Machine Learning (remote). National Physical Laboratory (NPL). 2023. London, UK.
• Beyond 0’s and 1’s ‑ Exploring the Complexities of Noise, Data Encoding, and Hyperparameter Optimization in Quantum Machine Learning. Walther Group. Faculty of Physics. University of Vienna. 2023. Vienna, Austria.
• Streaming IoT Data and the Quantum Edge: A Classic/Quantum Machine Learning Use Case. QuickPar Workshop at EuroPar 2023. Limassol, Cyprus.