Applications of Quantum Computing: Quantum Machine Learning, Optimization and CERN use-cases1h 30m
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.
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.
Carla Sophie Rieger(Technische Universitat Munchen (DE))
In this introduction to the foundations of quantum machine learning, we will dive into key concepts such as data encoding, feature map selection, and the comparison of different metrics for quantum kernel estimation. By the end of the session, students should be able to understand the basic steps and considerations in implementing and assessing quantum algorithms for machine learning.
Francesco Di Marcantonio
Francesco is a PhD student at CERN and University of the Basque Country. He studied at KTH and Politecnico di Torino and focuses on the simulation of Quantum Matter with Tensor Network Methods and other techniques related to Quantum Computation.
Roman is a Software Engineer with a background in Physics. He studied at ETH Zurich and does research in Quantum Computing and Particle Physics at CERN and the University of Tokyo.
MrFrancesco Di Marcantonio(UPV/EHU University of the Basque Country (SP)), Roman Luca Wixinger(ETH Zurich (CH))