



01578nas a2200157   4500008004100000245003800041210003700079260001500116300001100131490000800142520115100150100001901301700001901320700002201339856005901361       2016                        eng d00aQuantum-Enhanced Machine Learning0 aQuantumEnhanced Machine Learning  c2016/09/20  a1305010 v1173 a<p><span style="color: rgb(34, 34, 34); font-family: &quot;Helvetica Neue&quot;, Helvetica, Roboto, Arial, sans-serif; font-size: 14px;">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.</span></p>1 aDunjko, Vedran1 aTaylor, J., M.1 aBriegel, Hans, J.  uhttp://link.aps.org/doi/10.1103/PhysRevLett.117.130501