Quantum Machine Learning

Quantum computing has emerged as one of the most promising technologies of the 21st century, yet its practical use cases and programming paradigms remain unfamiliar. At the same time, artificial intelligence and machine learning are transforming research and industry at an unprecedented pace. The intersection of these two areas, Quantum Machine Learning (QML)  represents a rapidly expanding topic, but its concepts are still only partially understood and rarely taught in a coherent, hands-on framework.

Some of the most promising ideas in QML draw on methods and insights that are not widely known outside specialized research groups. Despite their potential, these approaches are rarely presented in summer schools or general machine learning conferences, leaving a significant knowledge gap for young researchers entering the field.

We aim to bridge this gap by providing junior scientists—particularly master’s students nearing graduation, PhD candidates, and early-career postdocs—with a structured introduction to Quantum Machine Learning. The program covers both fundamental concepts and advanced topics and Tensor Networks in AI, fostering interdisciplinary connections. Participants will gain a solid theoretical foundation along with practical, hands-on experience, including the opportunity to run experiments on IQM hardware during the school.

We have identified leading experts whose work sits at the convergence of quantum computing and AI, and who combine scientific excellence with the proven ability to deliver clear and engaging lectures suited to early-career researchers. All of them have agreed to contribute to the school, ensuring that participants receive a rigorous introduction.

Further information