Machine learning and quantum information processing: research directions and challenges - Peter Wittek

Machine learning uses efficient algorithms to uncover patterns in large data sets and it fundamentally changes our perspective on data processing. Efficient algorithms strike a balance between different facets of complexity: sample size, model and computational complexities. Approaching from the domain of quantum information processing, if we would like to use the rich theory of computational learning, we have several directions to pursue. We can apply a classical learning algorithm to a physics problem or we can come up with algorithms that use quantum resources either to solve classical learning problems or an application to physics. This talk gives an introduction to the core concepts in learning theory, and then looks at the major challenges ahead in the intersection of machine learning and quantum information processing.