Nonlinear optical components for all-optical probabilistic graphical model

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. Neifeld, N. Peyghambarian

Research output: Contribution to journalArticlepeer-review

10 Scopus citations


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.

Original languageEnglish (US)
Article number2128
JournalNature communications
Issue number1
StatePublished - Dec 1 2018

ASJC Scopus subject areas

  • Chemistry(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Physics and Astronomy(all)


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