Active learning machine learns to create new quantum experiments.

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Alexey A. Melnikov, Hendrik Poulsen Nautrup, Mario Krenn, Vedran Dunjko, Markus Tiersch, Anton Zeilinger, Hans J. Briegel

How useful can machine learning be in a quantum laboratory? Here we raise thequestion of the potential of intelligent machines in the context of scientificresearch. A major motivation for the present work is the unknown reachabilityof various entanglement classes in quantum experiments. We investigate thisquestion by using the projective simulation model, a physics-oriented approachto artificial intelligence. In our approach, the projective simulation systemis challenged to design complex photonic quantum experiments that producehigh-dimensional entangled multiphoton states, which are of high interest inmodern quantum experiments. The artificial intelligence system learns to createa variety of entangled states, and improves the efficiency of theirrealization. In the process, the system autonomously (re)discovers experimentaltechniques which are only now becoming standard in modern quantum opticalexperiments - a trait which was not explicitly demanded from the system butemerged through the process of learning. Such features highlight thepossibility that machines could have a significantly more creative role infuture research.

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