Quantum Kernel Methods (hands-on on Quask)

<!--HTML--><h1>Abstract</h1> 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. <h2>Bio</h2> <h3>Francesco Di Marcantonio</h3> 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. <h3>Roman Wixinger</h3> 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.