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  Active learning machine learns to create new quantum experiments

Melnikov, A. A., Nautrup, H. P., Krenn, M., Dunjko, V., Tiersch, M., Zeilinger, A., et al. (2018). Active learning machine learns to create new quantum experiments. Proceedings of the National Academy of Sciences of the United States of America, 115(6), 1221-1226. doi:10.1073/pnas.1714936115.

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Datensatz-Permalink: https://hdl.handle.net/21.11116/0000-0009-5EF0-F Versions-Permalink: https://hdl.handle.net/21.11116/0000-0009-5EF1-E
Genre: Zeitschriftenartikel

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 Urheber:
Melnikov, Alexey A.1, Autor
Nautrup, Hendrik Poulsen1, Autor
Krenn, Mario2, 3, 4, Autor
Dunjko, Vedran1, Autor
Tiersch, Markus1, Autor
Zeilinger, Anton1, Autor
Briegel, Hans J.1, Autor
Affiliations:
1external, ou_persistent22              
2University of Vienna, ou_persistent22              
3Austrian Academy of Sciences, ou_persistent22              
4External Organizations, ou_persistent22              

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Schlagwörter: machine learning; quantum experiments; quantum entanglement; artificial intelligence; quantum machine learning
 Zusammenfassung: 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.

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Sprache(n): eng - English
 Datum: 2018-02-06
 Publikationsstatus: Erschienen
 Seiten: -
 Ort, Verlag, Ausgabe: -
 Inhaltsverzeichnis: -
 Art der Begutachtung: -
 Identifikatoren: ISI: 000424191300048
DOI: 10.1073/pnas.1714936115
 Art des Abschluß: -

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Titel: Proceedings of the National Academy of Sciences of the United States of America
  Andere : PNAS
  Andere : Proceedings of the National Academy of Sciences of the USA
  Kurztitel : Proc. Natl. Acad. Sci. U. S. A.
Genre der Quelle: Zeitschrift
 Urheber:
Affiliations:
Ort, Verlag, Ausgabe: Washington, D.C. : National Academy of Sciences
Seiten: - Band / Heft: 115 (6) Artikelnummer: - Start- / Endseite: 1221 - 1226 Identifikator: ISSN: 0027-8424
CoNE: https://pure.mpg.de/cone/journals/resource/954925427230