Conference Agenda

Brief Description and Outline:

This tutorial aims to provide attendees with a foundational introduction to quantum computing and quantum machine learning (QML). It will address key challenges in QML, including data en- coding strategies, model trainability [1], the barren plateau phenomenon [2], optimization issues such as local minima [3], dequantization [4], benchmarking [5], current hardware limitations [6], etc. In addition, the tutorial will highlight recent and promising research directions in the field.

The tutorial will conclude with a project demonstrating a simple quantum application using Py- Torch/TensorFlow.

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1 Thanasilp, S., Wang, S., Nghiem, N.A. et al. Subtleties in the trainability of quantum machine learning models. Quantum Mach. Intell. 5, 21 (2023). https://doi.org/10.1007/s42484-023-00103-6

2 M. Larocca et al., “Barren Plateaus in Variational Quantum Computing,”Nat Rev Phys, vol. 7, no. 4, pp. 174–189, Mar. 2025, doi: 10.1038 s42254-025-00813-9.

3 X. You and X. Wu, “Exponentially Many Local Minima in Quantum Neural Networks,”Oct. 06, 2021, arXiv: arXiv:2110.02479. doi: 10.48550/arXiv.2110.02479.

4 R. Sweke et al., “Potential and limitations of random Fourier features for dequantizing quantum machine learning,”Quantum, vol. 9, p. 1640, Feb. 2025, doi: 10.22331/q-2025-02-20-

1640.

5 J. Bowles, S. Ahmed, and M. Schuld, “Better than classical? The subtle art of benchmarking quantum machine learning models,”Mar. 14, 2024, arXiv: arXiv:2403.07059. Accessed: Apr. 08, 2024. [Online]. Available: http://arxiv.org/abs/2403.07059 6 J. Preskill, “Quantum Computing in the NISQ era and beyond,”Quantum, vol. 2, p. 79, Aug. 2018, doi: 10.22331/q-2018-08-06-79.

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Goals:

The goal of the tutorial is to give participants an overview of current challenges, limitations and possibilities in the field of QML. After the tutorial, participants should be comfortable assessing current QML publications and should be able to deduce themselves which projects might benefit from integrating quantum computers in the short-, mid-, and long-term.

Quantum computers provide support for complex problems and can potentially offer exponentially faster solutions for some use cases. Although current quantum computers are still limited in their applicability in real-world projects, the technological and algorithmical outlook offer promising options. Furthermore,  awareness and knowledge needs to be built to enable good integration and use of quantum computers along their improving capabilities.

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Presenters Experience:

Eileen is a team leader on quantum machine learning at KIT. She received her PhD in computer science from the Karlsruhe Institute of Technology in 2017. She is used to work in close collaboration with diverse domains including for example High Energy Physics, Climatology, or Muselogy. Her research activities focus on trainability and efficiency of QML models, hybrid models and workflows, applied quantum computing, and (quantum) research software engineering. She has particular experience in training, teaching and supervision.

Since 2009 she has regular teaching experience and since 2022 she is teaching QML and Quantum Computing at KIT. She also has experience in giving tutorials for various audiences including primary pupils, pupils and participants at bachelors/masters/doctoral degrees at various occasions including summer schools.

Gabriel is a second-year PhD researcher focusing on the trainability of quantum machine learning models and the design of quantum algorithms for practical applications. Prior to his doctoral studies, he worked as a research assistant in numerical simulations of fluid dynamics and wave propagation. Since 2019, he has been involved in teaching algorithms, discrete mathematics, programming, parallel computing, and linear algebra. Since 2025, he has co-taught with Eileen the QML course at KIT.

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Target Audience:

Knowledge of linear algebra is recommended.

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Keywords:

Quantum Computing, Quantum Algorithms, Quantum Machine Learning