Abstract
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.
Bio
Roman is a Data Scientist at Ergon Informatik, specializing in software engineering for data science and research. He holds a Master’s degree in Physics from ETH Zurich. During his studies, he conducted research at CERN, TU Munich, and the University of Tokyo, focusing on quantum computing and particle physics.
