EF1 – Extracting dynamical Laws from Complex Data

Machine learning and specifically deep learning techniques are revolutionizing the way we think of algorithms making predictions or decisions based on training data and how we look at mathematical modeling in the first place. Machine learning is undoubtedly enjoying an enormous impact in a wide range of applications of image classification, language recognition, computer vision, and model building in physics and chemistry. Just as the practical development of algorithmic design in machine learning and artificial intelligence goes hand in hand with an improved mathematical understanding, there is another important development coming into play: This is the perspective of near-term quantum devices, instances of existing quantum computers, offering additional speedups in tasks of learning, exploiting coherence and quantum features in appropriately set up quantum algorithms. This development is triggered by the insights that suitable quantum devices are just becoming available and advantages are expected to become feasible in the near future. Indeed, there is no denying that the question in what way quantum-assisted machine learning may provide advantages over the best known classical algorithms is much in the focus of present research. In this enormously rapid development, providing a substantial body of heuristic studies, however, it is sometimes overlooked that a rigorous understanding of this emerging field of research is still painfully lacking. It is such a rigorous and careful approach that is going to be pursued here, putting an emphasis on the key make or break question in quantum-assisted machine learning: Can one prove a quantum advantage in learning?