%0 Generic %1 melnikov2017active %A Melnikov, Alexey A. %A Nautrup, Hendrik Poulsen %A Krenn, Mario %A Dunjko, Vedran %A Tiersch, Markus %A Zeilinger, Anton %A Briegel, Hans J. %D 2017 %K artificial_intelligence design_quantum_experiments journalclubqo machine_learning %R 10.1073/pnas.1714936115 %T Active learning machine learns to create new quantum experiments %U http://arxiv.org/abs/1706.00868 %X 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.

@misc{melnikov2017active, abstract = {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.}, added-at = {2018-03-27T14:00:40.000+0200}, author = {Melnikov, Alexey A. and Nautrup, Hendrik Poulsen and Krenn, Mario and Dunjko, Vedran and Tiersch, Markus and Zeilinger, Anton and Briegel, Hans J.}, biburl = {https://www.bibsonomy.org/bibtex/26433634cd7672e09bdf5ee72b6dca843/j.siemss}, description = {[1706.00868] Active learning machine learns to create new quantum experiments}, doi = {10.1073/pnas.1714936115}, interhash = {f68228fc7b009d267c3b969615d7c09e}, intrahash = {6433634cd7672e09bdf5ee72b6dca843}, keywords = {artificial_intelligence design_quantum_experiments journalclubqo machine_learning}, note = {cite arxiv:1706.00868Comment: 11 pages, 6 figures, 1 table; A. A. Melnikov and H. Poulsen Nautrup contributed equally to this work}, timestamp = {2018-03-27T14:00:40.000+0200}, title = {Active learning machine learns to create new quantum experiments}, url = {http://arxiv.org/abs/1706.00868}, year = 2017 }