title: Quantum-Enhanced Machine Learning.
abstract: The emerging field of quantum machine learning has the potential to substantially aid in the problems and scope of artificial intelligence. This is only enhanced by recent successes in the field of classical machine learning. In this work we propose an approach for the systematic treatment of machine learning, from the perspective of quantum information. Our approach is general and covers all three main branches of machine learning: supervised, unsupervised, and reinforcement learning. While quantum improvements in supervised and unsupervised learning have been reported, reinforcement learning has received much less attention. Within our approach, we tackle the problem of quantum enhancements in reinforcement learning as well, and propose a systematic scheme for providing improvements. As an example, we show that quadratic improvements in learning efficiency, and exponential improvements in performance over limited time periods, can be obtained for a broad class of learning problems.
authors.affiliation: Institut für Theoretische Physik, Universität Innsbruck, Technikerstraße 21a, A-6020 Innsbruck, Austria.
authors.forename: Vedran
authors.initials: V
authors.lastname: Dunjko
authors.affiliation: Joint Quantum Institute, National Institute of Standards and Technology, Gaithersburg, Maryland 20899, USA.|Joint Center for Quantum Information and Computer Science, University of Maryland, College Park, Maryland 20742, USA.
authors.forename: Jacob M
authors.initials: JM
authors.lastname: Taylor
authors.affiliation: Institut für Theoretische Physik, Universität Innsbruck, Technikerstraße 21a, A-6020 Innsbruck, Austria.
authors.forename: Hans J
authors.initials: HJ
authors.lastname: Briegel