Machine Learning (Prof. Behr)

Prinzipien der Quantenmechanik, Verschränkung, Unitäre Zeitentwicklung, Quantengatter und Quantenschaltung, Quantenalgorithmen und “Quantenvorteil”

The module DAT-B-CON-QUANT (Lecture and Lab Quantum Mechanics and Information Processing) combines computer and data science with the fundamentals of quantum mechanics and quantum computing.

The following topics are introduced:
Principles of quantum mechanics, entanglement, unitary time evolution, quantum gates and quantum switching, quantum algorithms and “quantum advantage”

This lecture delves into the modern methods of
machine learning (ML). In particular, methods of deep
learning and reinforcement learning are covered. Topics include theoretical foundations of deep learning
and reinforcement learning, architectures of artificial neural
networks, generative modeling, deep reinforcement
learning, automatic differentiation and relevant
optimization methods. Possible fields of application are
discussed, e.g. the processing of images or natural
language, anomaly detection or optimal control. For the
practical implementation of the concepts the course includes an introduction to the JAX library.