Active learning machine learns to create new quantum experiments
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- 18 January 2018
- journal article
- research article
- Published by Proceedings of the National Academy of Sciences in Proceedings of the National Academy of Sciences of the United States of America
- Vol. 115 (6), 1221-1226
- https://doi.org/10.1073/pnas.1714936115
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.Keywords
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Funding Information
- Austrian Science Fund (W1259-N27)
- Austrian Science Fund (SFB FoQuS F4012)
- Austrian Science Fund (DK-ALM)
- EC | FP7 | FP7 Ideas: European Research Council (SIQS Grant No. 600645 EU-FP7-ICT))
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