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Nonlinear optical components for all-optical probabilistic graphical model

Author

Listed:
  • Masoud Babaeian

    (University of Arizona
    University of Arizona)

  • Pierre-A. Blanche

    (University of Arizona)

  • Robert A. Norwood

    (University of Arizona)

  • Tommi Kaplas

    (University of Eastern Finland)

  • Patrick Keiffer

    (University of Arizona)

  • Yuri Svirko

    (University of Eastern Finland)

  • Taylor G. Allen

    (Georgia Institute of Technology)

  • Vincent W. Chen

    (Georgia Institute of Technology)

  • San-Hui Chi

    (Georgia Institute of Technology)

  • Joseph W. Perry

    (Georgia Institute of Technology)

  • Seth R. Marder

    (Georgia Institute of Technology)

  • Mark A. Neifeld

    (University of Arizona
    University of Arizona)

  • N. Peyghambarian

    (University of Arizona)

Abstract

The probabilistic graphical models (PGMs) are tools that are used to compute probability distributions over large and complex interacting variables. They have applications in social networks, speech recognition, artificial intelligence, machine learning, and many more areas. Here, we present an all-optical implementation of a PGM through the sum-product message passing algorithm (SPMPA) governed by a wavelength multiplexing architecture. As a proof-of-concept, we demonstrate the use of optics to solve a two node graphical model governed by SPMPA and successfully map the message passing algorithm onto photonics operations. The essential mathematical functions required for this algorithm, including multiplication and division, are implemented using nonlinear optics in thin film materials. The multiplication and division are demonstrated through a logarithm-summation-exponentiation operation and a pump-probe saturation process, respectively. The fundamental bottlenecks for the scalability of the presented scheme are discussed as well.

Suggested Citation

  • Masoud Babaeian & Pierre-A. Blanche & Robert A. Norwood & Tommi Kaplas & Patrick Keiffer & Yuri Svirko & Taylor G. Allen & Vincent W. Chen & San-Hui Chi & Joseph W. Perry & Seth R. Marder & Mark A. Ne, 2018. "Nonlinear optical components for all-optical probabilistic graphical model," Nature Communications, Nature, vol. 9(1), pages 1-8, December.
    • Handle: RePEc:nat:natcom:v:9:y:2018:i:1:d:10.1038_s41467-018-04578-x
    DOI: 10.1038/s41467-018-04578-x
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