



@article {1911,
	title = {Quantum-Enhanced Machine Learning},
	journal = {Physical Review Letters},
	volume = {117},
	year = {2016},
	month = {2016/09/20},
	pages = {130501},
	abstract = {<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>},
	doi = {10.1103/PhysRevLett.117.130501},
	url = {http://link.aps.org/doi/10.1103/PhysRevLett.117.130501},
	author = {Dunjko, Vedran and J. M. Taylor and Briegel, Hans J.}
}
