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Cited by 296

The DOI matches its Crossref record; 14 of the 14 references it cites were found in a registry or an index.

Active learning machine learns to create new quantum experiments

Open access
Melnikov, Alexey A., Poulsen Nautrup, Hendrik, Krenn, Mario, Dunjko, Vedran, Tiersch, Markus, Zeilinger, Anton, Briegel, Hans J.·2018·Proceedings of the National Academy of Sciences
DOI 10.1073/pnas.1714936115
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Abstract

Significance: Quantum experiments push the envelope of our understanding of fundamental concepts in quantum physics. Modern experiments have exhaustively probed the basic notions of quantum theory. Arguably, further breakthroughs require the tackling of complex quantum phenomena and consequently require complex experiments and involved techniques. The designing of such complex experiments is difficult and often clashes with human intuition. We present an autonomous learning model which learns to design such complex experiments, without relying on previous knowledge or often flawed intuition. Our system not only learns how to design desired experiments more efficiently than the best previous approaches, but in the process also discovers nontrivial experimental techniques. Our work demonstrates that learning machines can offer dramatic advances in how experiments are generated.

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Research works printed but not linked: 42

  1. 10.1038/nphys4037 · DOI · Paper page
  2. 10.1126/science.1165893 · DOI · Paper page
  3. 10.1038/nphys4053 · DOI
  4. S Russel, P Norvig Artificial Intelligence–A Modern Approach (Prentice Hall, 3rd Ed, Upper Saddle River, NJ, 2010).
  5. 10.1103/PhysRevLett.116.090405 · DOI · Paper page
  6. 10.1038/nphoton.2016.12 · DOI · Paper page
  7. 10.1088/1367-2630/18/4/043019 · DOI
  8. 10.1103/PhysRevLett.119.180510 · DOI · Paper page
  9. M Erhard M Malik M Krenn A Zeilinger Experimental GHZ entanglement beyond qubits. arXiv:1708.03881. (2017). · arXiv
  10. 10.1103/PhysRevLett.118.080401 · DOI
  11. 10.1103/PhysRevLett.119.240403 · DOI
  12. 10.1038/srep00400 · DOI
  13. 10.1103/PhysRevA.89.012105 · DOI
  14. 10.1103/PhysRevLett.110.030501 · DOI · Paper page
  15. 10.1103/PhysRevA.88.042328 · DOI
  16. 10.1103/RevModPhys.81.865 · DOI · Paper page
  17. 10.1103/PhysRevA.69.062311 · DOI · Paper page
  18. 10.1103/PhysRevLett.87.117901 · DOI · Paper page
  19. 10.1103/RevModPhys.81.1301 · DOI · Paper page
  20. 10.1038/35085529 · DOI · Paper page
  21. 10.1038/nphys607 · DOI · Paper page
  22. 10.1098/rsta.2015.0442 · DOI
  23. 10.1038/nphys1996 · DOI · Paper page
  24. 10.1073/pnas.1402365111 · DOI · Paper page
  25. 10.1103/PhysRevLett.116.073601 · DOI
  26. 10.1038/nature14246 · DOI · Paper page
  27. 10.1007/s00354-015-0102-0 · DOI
  28. AA Melnikov A Makmal HJ Briegel Projective simulation applied to the grid-world and the mountain-car problem. arXiv:1405.5459. (2014). · arXiv · doi:10.5430/air.v3n3p24
  29. ØF Bjerland Projective Simulation compared to reinforcement learning. Master’s thesis (Department of Computer Science University of Bergen Bergen Norway. (2015).
  30. 10.1038/s41598-017-14740-y · DOI
  31. S Hangl, E Ugur, S Szedmak, J Piater, Robotic playing for hierarchical complex skill learning. Proc IEEE/RSJ Int Conf Intell Robots Syst (IEEE, New York), pp. 2799–2804 (2016).
  32. GD Paparo, V Dunjko, A Makmal, MA Martin-Delgado, HJ Briegel, Quantum speed-up for active learning agents. Phys Rev X 4, 031002 (2014).
  33. 10.1088/1367-2630/17/2/023006 · DOI
  34. 10.1038/srep18036 · DOI
  35. P Mirowski Learning to navigate in complex environments. arXiv: 1611.03673. (2016). · arXiv
  36. T Mannucci, EJ van Kampen, A hierarchical maze navigation algorithm with reinforcement learning and mapping. Proc IEEE Symp Ser Comput Intelligence (IEEE, New York), pp. 1–8 (2016).
  37. 10.1038/srep00522 · DOI
  38. 10.1109/ACCESS.2016.2556579 · DOI
  39. 10.1103/PhysRevLett.88.257901 · DOI · Paper page
  40. 10.1070/PU1988v031n01ABEH002537 · DOI
  41. 10.1080/09500340.2014.899645 · doi:10.1080/09500340.2014.899645
  42. K Schwab The Fourth Industrial Revolution (Penguin, London, 2017).

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Metadata

Authors
Melnikov, A. A., Poulsen Nautrup, H., Krenn, M., Dunjko, V., Tiersch, M., Zeilinger, A., & Briegel, H. J.
Venue
Proceedings of the National Academy of Sciences
Publisher
Proceedings of the National Academy of Sciences
Published
2018-01-18
Volume
115
Issue
6
Pages
1221-1226
ISSN
  • 0027-8424 (ISSN-L)
  • 1091-6490
DOI
10.1073/pnas.1714936115
OpenAlex
W2624637765
Cited by
296
References
14
FWCI
22.63
Type
journal article
Language
en
Open access
bronze
Licence
www.pnas.org/site/aboutpnas/licenses.xhtml (vor, 181 d)
Funders
  • Austrian Science Fund (W1259-N27, SFB FoQuS F4012, DK-ALM)
  • EC | FP7 | FP7 Ideas: European Research Council (SIQS Grant No. 600645 EU-FP7-ICT))

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CoChat. (2026). Citation check: Active learning machine learns to create new quantum experiments. https://app.cochat.ai/papers/10.1073/pnas.1714936115
The paper
Melnikov, A. A., Poulsen Nautrup, H., Krenn, M., Dunjko, V., Tiersch, M., Zeilinger, A., & Briegel, H. J. (2018). Active learning machine learns to create new quantum experiments. Proceedings of the National Academy of Sciences. https://doi.org/10.1073/pnas.1714936115

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

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