Quantum Machine Learning

In this course, participants learn the essentials of Quantum Computing. We start by outlining the conceptual foundations of quantum systems. The next chapter focuses on the basic elementary computational operations, with example programs in Python qiskit. Using these building blocks, we introduce some of the core quantum computing algorithms, with a focus on coherent quantum machine learning. Examples are Quantum Fourier Transformation, Quantum Phase Estimation and Grover search. Next, we introduce the concept of Near-Term Intermediate Scale Quantum devices (NISQ) and derive hybrid quantum-classical algorithms that are suitable for running on current hardware. Hereby, we also discuss the recent breakthroughs and quantum supremacy, and discuss existing hardware. Quantum computing intuition often requires some mathematical intuition. The course requires a medium to strong theoretical background, but is accessible for people who are unfamiliar with quantum mechanics and quantum computing. The course provides a sound mix of both theoretical and technical insights, as well as practical implementation details.