Quantum-Enhanced Machine Learning

Physical Review Letters
Vedran DunjkoHans J Briegel


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.


Nov 2, 2017·Scientific Reports·Alexey A MelnikovHans J Briegel
Mar 6, 2018·Reports on Progress in Physics·Vedran Dunjko, Hans J Briegel
Jul 20, 2018·PloS One·F A Cárdenas-LópezEnrique Solano
Sep 15, 2017·Nature·Jacob BiamonteSeth Lloyd
May 27, 2017·Physical Review Letters·Alex MonràsPeter Wittek
May 13, 2017·Physical Review Letters·Lauretta R SchwarzGeorge H Booth
Jul 27, 2018·Nature·Keith T ButlerAron Walsh
Mar 2, 2019·Dialectica·Thomas Müller, Hans J Briegel
Oct 24, 2017·Scientific Reports·Unai Alvarez-RodriguezEnrique Solano
Jan 25, 2020·Physical Review Letters·Dan-Bo ZhangZ D Wang
Feb 12, 2020·Nature Communications·Kerstin BeerRamona Wolf
Jan 22, 2020·Nature Human Behaviour·Ji-An LiXiaochu Zhang


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