## Abstract

This talk introduces the fundamental concepts of quantum machine learning (QML). In the realm of parametrised quantum circuits, embedding of classical data and parameter optimization methods as part of the general data processing pipeline for quantum networks are being discussed. Furthermore, possible advantages and challenges in the QML domain are considered and the presentation concludes with examples of CERN specific use-cases.

## Bio

Carla is a theoretical physicist specializing in quantum computing and quantum algorithms. With a master's degree from ETH Zurich, Carla is currently pursuing a Ph.D. at CERN with TUM, focusing on quantum algorithms for combinatorial problems and efficient classical simulability of quantum circuits