In recent years remarkable progress has been made in the field of artificial intelligence (AI). Machine learning (ML) is a sub-discipline of AI that seeks to develop techniques that enable computers to learn from data. The goal of ML methods is to reliably abstract the underlying model for specific tasks. Quantum computing describes information processing using devices based on the laws of quantum theory. Based on the success of ML and quantum computing so far, it can be expected that both technologies will play a huge role in digital computing in the future. Therefore, it is exciting to find out how these two techniques can be combined to provide better and reliable solutions for various tasks.

Quantum Machine Learning (QML) is an interdisciplinary research area that spans physics, mathematics, computer science, and electrical engineering. It is dedicated to the use of quantum computers to compute machine learning algorithms. Methods of QML help improve classical methods of ML by taking advantage of quantum computing. By using QML, not only are previous tasks solved faster, but it is also possible to incorporate more aspects of the natural world into existing AI methods.

The module covers the fundamentals and concepts of Quantum Machine Learning. Topics covered include:

• Basic concepts of quantum mechanics.

• From bits to QBits

• Quantum computing and quantum circuits

• Quantum Information Theory

• Quantum Signal Processing

• Review of classical machine learning

• Quantum algorithms

• Quantum classification and regression

• Quantum Deep Learning

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• ... other interesting topics.

The exam takes place as an oral examination with a duration of 20 minutes.

The module grade is the grade of the oral examination.