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Quantum Machine Learning SC 2024

DescriptionLeveraging quantum algorithms holds the potential for exponential speedup and disruptive new approaches in machine learning. In this SC tutorial, we present an introduction to quantum computing with a focus on its application to machine learning. It is aimed at computer- and data scientists and HPC users of any scientific domain. Our focus is to show how quantum computers are programmed today, while providing the essential math and physics background required to develop quantum algorithms. Our tutorial contains two parts. The first part introduces basic ideas and examples of gate-based quantum computing designed for participants with only basic knowledge of quantum computing. The second part illustrates quantum classifiers, such as the Variational Quantum Classifier, as an introduction to Quantum Machine Learning. The tutorial builds on our successful tutorials at ISC by refining the parts that were challenging for participants. This tutorial will use the Qiskit framework to show how quantum algorithms can be constructed from available building blocks. As participants progress through this tutorial, they will acquire practical insights into the complexities of quantum computing, equipping them with the fundamental skills to explore the potential of quantum machine learning in their respective fields.

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