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Active learning machine learns to create new quantum experiments.

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.

journal_name

Proc Natl Acad Sci U S A

journal_title

Proceedings of the National Academy of Sciences of the United States of America

authors

Melnikov AA,Poulsen Nautrup H,Krenn M,Dunjko V,Tiersch M,Zeilinger A,Briegel HJ

doi

10.1073/pnas.1714936115

subject

Has Abstract

pub_date

2018-02-06 00:00:00

pages

1221-1226

issue

6

eissn

0027-8424

issn

1091-6490

pii

1714936115

journal_volume

115

pub_type

杂志文章

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