2024W

• Hybrid Quantum - Classical Systems / 194.027 / VU

• Exploring Channel Distinguishability in Local Neighborhoods of the Model Space in Quantum Neural Networks / Herbst, S., Cranganore, S. S., De Maio, V., & Brandic, I. (2024). Exploring Channel Distinguishability in Local Neighborhoods of the Model Space in Quantum Neural Networks. arXiv. https://doi.org/10.48550/arXiv.2410.09470
  Projects: HPQC (2023–2025) / Themis FWF (2024–2027) / TRITON FWF (2023–2027)
• Streaming IoT Data and the Quantum Edge: A Classic/Quantum Machine Learning Use Case / 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
  Projects: HPQC (2023–2025) / RUCON (2016–2023) / TRITON FWF (2023–2027)
• On Optimizing Hyperparameters for Quantum Neural Networks / Herbst, S., De Maio, V., & Brandic, I. (2024). On Optimizing Hyperparameters for Quantum Neural Networks. arXiv. https://doi.org/10.48550/arXiv.2403.18579
  Projects: HPQC (2023–2025) / TRITON FWF (2023–2027)
• Beyond 0’s and 1’s: Exploring the impact of noise, data encoding, and hyperparameter optimization in quantum machine learning / Herbst, S. (2023). Beyond 0’s and 1’s: Exploring the impact of noise, data encoding, and hyperparameter optimization in quantum machine learning [Diploma Thesis, Technische Universität Wien]. reposiTUm. https://doi.org/10.34726/hss.2023.111504
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