Conference Agenda

Brief Description and Outline:

This is a hands-on tutorial to create a hybrid quantum-classical QML workflow for training a Quantum Neural Network. We will first introduce different strategies on how to include quantum computers into machine learning workflows. Further, we will not only show how an optimization/training process looks like, but will also discuss different data encoding options and how to choose an appropriate QML model. Furthermore, we will look into how to interpret the output of quantum computers.

The project itself will be realized with Pennylane, a differentiable programming framework that is optimized for integration in high performance clusters. We will further interface with common tools such as PyTorch and TensorFlow.

Goals:

Goal of the tutorial is to lower the barriers for starting to develop and research hybrid quantum-classical workflows. As a current and upcoming technology with the potential to revolutionize machine learning, it is critical for researchers to understand both the limitations and well as potential of this field.

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 Museology. 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.

Target Audience:

Participants should have experience in programming in python. We expect participants to either have a basic understanding of quantum computing already or to participate in the first part of the tutorial.

Each participant should bring a laptop.

Keywords:

Quantum Machine Learning, Quantum Variational Algorithms, Quantum Neural Networks