Machine learning (ML) is one of the fastest-progressing research directions in applied computer science. The field investigates the development of algorithms that can learn from data by fitting a collection of model parameters to the data via iterative optimization of an objective function. The selection of a model structure, be it a neural network or kernel function, is a problem-dependent task often made by hand. But there exist Auto ML systems [14] that can choose a model automatically depending solely on the input data and the task at hand. Quantum computing (QC) studies how hard computational problems can be efficiently solved using quantum mechanics. A large-scale error-corrected quantum computer can solve computational problems that don't have a classical solution. A prime example of that is Shor's algorithm [30] for integer factorization. The "holy grail" of applied QC is the so-called quantum supremacy or quantum advantage. That is the name for a technological milestone marking the moment when quantum machines will solve a specific task faster than the most advanced supercomputer.