This module aims to introduce students to the theoretical and practical aspects of using quantum computers for machine learning. In the first part of the lecture, the necessary mathematical foundations of quantum systems and their representation through qubits and quantum circuits will be summarized. Based on well-known quantum algorithms, the advantages and possibilities of quantum computing will be demonstrated. Finally, an overview of current hybrid approaches in the field of Quantum Machine Learning and their applications and limitations will be provided:

• Fundamentals and basic concepts
  • Theoretical and practical foundations of quantum computing
  • Taxonomy of Quantum Machine Learning
• Overview of QML algorithms, e.g.
  • Variational Quantum Eigensolver
  • Quantum Approximate Optimization Algorithm
  • Quantum Autoencoder
  • Quantum Convolutional Neural Networks
  • Quantum Generative Adversarial Neural Networks
  • Quantum Kernels
• Current challenges, e.g.
  • Noise
  • Barren Plateaus

In particular, the module will examine the applicability of today's quantum computers and the scalability of the presented approaches.