  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.
