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A Nanophotonic Computing Paradigm: Problem-Solving and Decision-Making Systems Using Spatiotemporal Photoexcitation Transfer Dynamics

  • Masashi AonoEmail author
  • Song-Ju KimEmail author
  • Makoto Naruse
  • Masamitsu Wakabayashi
  • Hirokazu Hori
  • Motoichi Ohtsu
  • Masahiko Hara
Chapter
Part of the Nano-Optics and Nanophotonics book series (NON)

Abstract

In contrast to conventional digital computers that operate as instructed by programmers, biological organisms solve problems and make decisions through intrinsic spatiotemporal dynamics in which their dynamic components process environmental information in a self-organized manner. Previously, we formulated two mathematical models of spatiotemporal dynamics by which the single-celled amoeba (a plasmodial slime mold), which exhibits complex spatiotemporal oscillatory dynamics and sophisticated computing capabilities, could solve a problem and make a decision by changing its amorphous shape in dynamic and uncertain environments. These models can also be implemented by various physical systems that exhibit suitable spatiotemporal dynamics resembling the amoeba’s shape-changing capability. Here we demonstrate that the photoexcitation transfer phenomena in certain quantum nanostructures mediated by optical near-field interactions mimic the amoeba-like spatiotemporal dynamics and can be used to solve two highly complex problems; the satisfiability problem, which is one of the most difficult combinatorial optimization problems, to determine whether a given logical proposition is self-consistent, and the multi-armed bandit problem, which is a decision-making problem in finding the most profitable option from among a number of options that provide rewards with different unknown probabilities. Our problem-solving and decision-making models exhibited better performances than conventionally known best algorithms. These demonstrations pave the way for a novel nanophotonic computing paradigm in which both coherent and dissipative processes are exploited for performing powerful solution searching and efficient decision-making with low energy consumption.

Keywords

Slot Machine Lower Energy Level Satisfying Assignment Radiation Probability Bandit Problem 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    M. Conrad, Commun. ACM 28, 464–480 (1985)CrossRefGoogle Scholar
  2. 2.
    M. Dorigo, V. Maniezzo, A. Colorni, IEEE Trans. Syst. Man Cybernet. B 26, 29–41 (1996)CrossRefGoogle Scholar
  3. 3.
    E. Bonabeau, M. Dorigo, G. Theraulaz, Swarm Intelligence: From Natural to Artificial Systems (Oxford University Press, New York, 1999)zbMATHGoogle Scholar
  4. 4.
    G. Rozenberg, T. Back, J. Kok (eds.), Handbook of Natural Computing (Springer, Netherlands, 2012)Google Scholar
  5. 5.
    T. Nakagaki, H. Yamada, A. Toth, Nature 407, 470 (2000)ADSCrossRefGoogle Scholar
  6. 6.
    A. Tero, S. Takagi, T. Saigusa, K. Ito, D.P. Bebber, M.D. Fricker, K. Yumiki, R. Kobayashi, T. Nakagaki, Science 327(5964), 439–442 (2010)Google Scholar
  7. 7.
    M. Aono, Y.-P. Gunji, BioSystems 71, 257–287 (2003)CrossRefGoogle Scholar
  8. 8.
    M. Aono, Hara, K. Aihara, Commun. ACM 50(9), 69–72 (2007)Google Scholar
  9. 9.
    M. Aono, Y. Hirata, M. Hara, K. Aihara, New Generat Comput 27, 129–157 (2009)CrossRefzbMATHGoogle Scholar
  10. 10.
    A. Takamatsu, R. Tanaka, H. Yamada, T. Nakagaki, T. Fujii, I. Endo, Phys. Rev. Lett. 87(7), 078102 (2001)ADSCrossRefGoogle Scholar
  11. 11.
    S. Takagi, T. Ueda, Physica D 237, 420–427 (2008)ADSCrossRefGoogle Scholar
  12. 12.
    M. Aono, S.-J. Kim, L. Zhu, M. Wakabayashi, M. Hara, Submitted (2013)Google Scholar
  13. 13.
    M. Aono, S.-J. Kim, L. Zhu, M. Naruse, M. Ohtsu, H. Hori, M. Hara, in Proceedings of 2012 International Conference on Nonlinear Theory and its Applications, Majorca, 22–26 October 2012, pp. 586–589Google Scholar
  14. 14.
    S.-J. Kim, M. Aono, Hara. BioSystems 101, 29–36 (2010)Google Scholar
  15. 15.
    S.-J. Kim, E. Nameda, M. Aono, M. Hara, in Proceedings of 2011 International Conference on Nonlinear Theory and its Applications, Kobe, 4–7 September 2011, pp. 176–179Google Scholar
  16. 16.
    S.-J. Kim, M. Aono, E. Nameda, M. Hara, Technical Report of IEICE (CCS-2011-025) [in Japanese], 2011, pp. 36–41Google Scholar
  17. 17.
    S.-J. Kim, M. Aono, E. Nameda, M. Hara, in Proceedings of 2012 International Conference on Nonlinear Theory and its Applications, Majorca, 22–26 October 2012, pp. 590–593Google Scholar
  18. 18.
    M. Naruse, T. Miyazaki, T. Kawazoe, S. Sangu, K. Kobayashi, F. Kubota, M. Ohtsu, IEICE Trans. Electron. E88-C, 1817–1823 (2005)Google Scholar
  19. 19.
    M. Naruse, M. Aono, S.-J. Kim, T. Kawazoe, W. Nomura, H. Hori, M.M. Hara, Ohtsu. Phys. Rev. B 86, 125407 (2012)ADSCrossRefGoogle Scholar
  20. 20.
    M. Aono, M. Naruse, S.-J. Kim, M. Wakabayashi, H. Hori, M. Ohtsu, M. Hara, Langmuir 29, 7557–7564 (2013)Google Scholar
  21. 21.
    S.-J. Kim, M. Naruse, M. Aono, M. Ohtsu, M. Hara, Sci. Rep. 3, 2370 (2013). doi: 10.1038/srep02370
  22. 22.
    M. Ohtsu, T. Kawazoe, T. Yatsui, M. Naruse, IEEE J. Sel. Top. Quant. Electron 14, 1404–1417 (2009)CrossRefGoogle Scholar
  23. 23.
    T. Kawazoe, K. Kobayashi, K. Akahane, M. Naruse, N. Yamamoto, M. Ohtsu, Appl. Phys. B 84, 243–246 (2006)Google Scholar
  24. 24.
    T. Yatsui, S. Sangu, T. Kawazoe, M. Ohtsu, S.J. An, J. Yoo, G.-C. Yi, Appl. Phys. Lett. 90(223110), 1–3 (2007)Google Scholar
  25. 25.
    W. Nomura, T. Yatsui, T. Kawazoe, M. Naruse, M. Ohtsu, Appl. Phys. B 100, 181–187 (2010)ADSCrossRefGoogle Scholar
  26. 26.
    T. Kawazoe, M. Ohtsu, S. Aso, Y. Sawado, Y. Hosoda, K. Yoshizawa, K. Akahane, N. Yamamoto, M. Naruse, Appl. Phys. B 103, 537–546 (2011)Google Scholar
  27. 27.
    K. Akahane, N. Yamamoto, M. Tsuchiya, Appl. Phys. Lett. 93, 041121 (2008)ADSCrossRefGoogle Scholar
  28. 28.
    C. Pistol, C. Dwyer, A.R. Lebeck, IEEE Micro. 28, 7–18 (2008)CrossRefGoogle Scholar
  29. 29.
    M. Naruse, H. Hori, K. Kobayashi, P. Holmström, L. Thylén, M. Ohtsu, Opt. Express 18, A544–A553 (2010)CrossRefGoogle Scholar
  30. 30.
    M. Ohtsu, K. Kobayashi, T. Kawazoe, T. Yatsui, M. Naruse, M., Principles of Nanophotonics (Taylor and Francis, Boca Raton, 2008)CrossRefGoogle Scholar
  31. 31.
    T. Itoh, M. Furumiya, T. Ikehara, C. Gourdon, Solid State Commun. 73, 2710–274 (1990)Google Scholar
  32. 32.
    M.R. Garey, D.S. Johnson, Computers and Intractability: A Guide to the Theory of NP-Completeness (W. H. Freeman and co., New York, 1979)zbMATHGoogle Scholar
  33. 33.
    H.J. Carmichael, Statistical Methods in Quantum Optics 1 (Springer, Berlin, 1999)CrossRefzbMATHGoogle Scholar
  34. 34.
    U. Schöning, in Proceedings of 40th Symposium on Foundations of Computer Science, 1999, pp. 410–414Google Scholar
  35. 35.
    K. Iwama, S. Tamaki, in Proceedings 15th Symposium on Discrete Algorithms, 2004, p. 328Google Scholar
  36. 36.
    http://www.cs.ubc.ca/ hoos/SATLIB/benchm.html
  37. 37.
    S. Kirkpatrick, B. Selman, Science 264, 1297–1301 (1994)MathSciNetADSCrossRefzbMATHGoogle Scholar
  38. 38.
    R. Monasson, R. Zecchina, S. Kirkpatrick, B. Selman, L. Troyansky, Nature 400, 133–137 (1999)MathSciNetADSCrossRefGoogle Scholar
  39. 39.
    H. Robbins, Bull. Amer. Math. Soc. 58, 527–536 (1952)MathSciNetCrossRefzbMATHGoogle Scholar
  40. 40.
    W. Thompson, Biometrika 25, 285–294 (1933)zbMATHGoogle Scholar
  41. 41.
    J. Gittins, D. Jones, A dynamic allocation index for the sequential design of experiments, in Progress in Statistics, ed. by J. Gans (North Holland, Amsterdam, 1974) pp. 241–266Google Scholar
  42. 42.
    J. Gittins, J.R. Stat, Soc. B 41, 148–177 (1979)zbMATHGoogle Scholar
  43. 43.
    R. Agrawal, Adv. Appl. Prob. 27, 1054–1078 (1995)CrossRefzbMATHGoogle Scholar
  44. 44.
    P. Auer, N. Cesa-Bianchi, P. Fischer, Mach. Learn. 47, 235–256 (2002)CrossRefzbMATHGoogle Scholar
  45. 45.
    L. Kocsis, C. Szepesvári, in ECML2006, Lecture notes in artificial intelligence, vol. 4212 (Springer, Berlin, 2006), pp. 282–293Google Scholar
  46. 46.
    S. Gelly, Y. Wang, R. Munos, O. Teytaud, O. RR-6062-INRIA, pp. 1–19 (2006)Google Scholar
  47. 47.
    N. Daw, J. O’Doherty, P. Dayan, B. Seymour, R. Dolan, Nature 441, 876–879 (2006)ADSCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Masashi Aono
    • 1
    Email author
  • Song-Ju Kim
    • 2
    Email author
  • Makoto Naruse
    • 3
    • 4
  • Masamitsu Wakabayashi
    • 5
  • Hirokazu Hori
    • 6
  • Motoichi Ohtsu
    • 4
    • 7
  • Masahiko Hara
    • 8
  1. 1.Earth-Life Science InstituteTokyo Institute of TechnologyMeguro-kuJapan
  2. 2.Atomic Electronics Group, WPI Center for Materials Nanoarchitectonics (MANA)National Institute for Materials Science (NIMS)TsukubaJapan
  3. 3.Photonic Network Research InstituteNational Institute of Information and Communications TechnologyTokyoJapan
  4. 4.Nanophotonics Research Center, Graduate School of EngineeringThe University of TokyoBunkyo-kuJapan
  5. 5.Department of Biomolecular EngineeringTokyo Institute of TechnologyMidori-kuJapan
  6. 6.Interdisciplinary Graduate School of Medicine and EngineeringUniversity of YamanashiKofuJapan
  7. 7.Department of Electrical Engineering and Information Systems Graduate School of EngineeringThe University of TokyoBunkyo-kuJapan
  8. 8. Department of Electronic Chemistry, Interdisciplinary Graduate School of Science and EngineeringTokyo Institute of TechnologyMidori-kuJapan

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