Tensor networks and machine learning for approximating and optimizing functions in quantum physics

In this thesis, we explore the intersection of informatics and mathematics to address numerical problems in quantum physics. We introduce, analyze and evaluate novel methods for the approximation of physical quantities and the optimization of performance criteria in quantum control. These methods are based on techniques from the fields of tensor networks, numerical analysis and machine learning. Furthermore, we present work on the relation between machine learning and tensor network methods.