TY - JOUR AB - How useful can machine learning be in a quantum laboratory? Here we raise the question of the potential of intelligent machines in the context of scientific research. A major motivation for the present work is the unknown reachability of various entanglement classes in quantum experiments. We investigate this question by using the projective simulation model, a physics-oriented approach to artificial intelligence. In our approach, the projective simulation system is challenged to design complex photonic quantum experiments that produce high-dimensional entangled multiphoton states, which are of high interest in modern quantum experiments. The artificial intelligence system learns to create a variety of entangled states and improves the efficiency of their realization. In the process, the system autonomously (re)discovers experimental techniques which are only now becoming standard in modern quantum optical experiments—a trait which was not explicitly demanded from the system but emerged through the process of learning. Such features highlight the possibility that machines could have a significantly more creative role in future research. AU - Melnikov, Alexey A. AU - Nautrup, Hendrik Poulsen AU - Krenn, Mario AU - Dunjko, Vedran AU - Tiersch, Markus AU - Zeilinger, Anton AU - Briegel, Hans J. DA - 2018/02/06/ DO - 10.1073/pnas.1714936115 ET - 2023/03/14/ IS - 6 JF - PNAS PY - 2018 SE - 2018/02/06/ SP - 1221-1226 TI - Active learning machine learns to create new quantum experiments UR - http://www.pnas.org/content/early/2018/01/17/1714936115.short VL - 115 ER -