In this project, we investigate the foundations of Quantum Machine Learning and its implementation for computational taks in engineering such as regression or classification. We start with an example for each: tabular data and images; then we will spend most of the time on Physics-informed Neural Networks for examples from computational solid or fluid mechancis. The goal is to gain an understanding of the potentials and limitations of Quantum Machine Learning compared to traditional ML/DL for prototypical engineering computation tasks.

You will delve into the realm of quantum machine and deep learning and its application to engineering tasks. The heart of the approach lies in coding examples and gaining first hands-on experience with quantum machine learning and then be able to judge its computational complexity and accuracy. Join us on this journey at the intersection of quantum physics, machine and deep learning and computational engineering science, where we aim to unlock the secrets hidden behind quantum AI.