Supervisor: Ove Christiansen

Machine Learning (ML) has become a central paradigm in modern science, enabling
pattern recognition and predictive modelling across large datasets in physics, from highenergy
collision classification to quantum many-body analysis. Yet, classical ML faces
challenges related to data dimensionality, interpretability, and computational cost. Quantum
Machine Learning (QML) seeks to overcome these limitations by leveraging the exponential
richness of Hilbert space and the natural linear algebra of quantum systems.
Using variational quantum circuits and quantum kernels, QML can process information
geometrically and potentially offer advantages in expressiveness and scalability. This presentation
introduces key ML concepts, their applications in physics, and outlines how
quantum algorithms may transform data-driven discovery in the NISQ era.