All-optical graphical models for probabilistic inference

Pierre Alexandre Blanche, Madeleine Glick, John W Wissinger, Khanh Q Kieu, Masoud Babaeian, Houman Rastegarfar, Veysi Demir, Mehmetcan Akbulut, Patrick Keiffer, Robert A Norwood, Nasser N Peyghambarian, Mark A Neifeld

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Scopus citations


Considering that high performance electronic computation has become extremely efficient, for an optical hardware accelerator to be relevant, it must solve a type or a set of problems where its electronic counterpart is still struggling in term of size, energy, or time. We have identified one such challenge as the minimization of large scale Ising Hamiltonians when the number of particles is on the order of a million. Here we discuss an algorithmic approach based on probabilistic inference using graphical model and message passing.

Original languageEnglish (US)
Title of host publication2016 IEEE Photonics Society Summer Topical Meeting Series, SUM 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages2
ISBN (Electronic)9781509019007
StatePublished - Aug 22 2016
Event2016 IEEE Photonics Society Summer Topical Meeting Series, SUM 2016 - Newport Beach, United States
Duration: Jul 11 2016Jul 13 2016


Other2016 IEEE Photonics Society Summer Topical Meeting Series, SUM 2016
CountryUnited States
CityNewport Beach

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Signal Processing
  • Atomic and Molecular Physics, and Optics
  • Electrical and Electronic Engineering

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