Ultrafast Boltzmann Sampling using Photonic Ising Machines for Machine Learning

Guy Van Der Sande, Fabian Böhm, Diego Alonso-Urquijo Iruarrizaga, Guy Verschaffelt

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


Ising machines have emerged as a promising computational concept that specializes in solving resource intensive optimization problems. Their operation is based on mapping a problem’s cost function to a network of coupled Ising spins whose energy is described by the Ising Hamiltonian. Such a spin system can be emulated on a network of bistable analog oscillators and the tendency of this system to evolve to the lowest energy configuration is then used to find optimal solutions of a problem orders of magnitude faster than digital computers. In optics, photonic Ising machines based on coupled lasers, degenerate optical parametric oscillators and polariton condensates have recently demonstrated potential performance gains over digital hardware. As an alternative to these complex systems, we have developed a photonic Ising machine based on opto-electronic oscillators [1], which can utilize telecom-grade components and potentially yields high bandwidths of 40 GHz. We have shown that this opto-electronic Ising machine can have similar and in specific cases even significantly better performance on solving combinatorial optimization tasks as compared to the state of the art.
Original languageEnglish
Title of host publicationConference on Lasers and Electro-Optics/Europe (CLEO/Europe 2023) and European Quantum Electronics Conference (EQEC 2023)
PublisherOptica Publishing Group
Number of pages1
ISBN (Electronic)979-8-3503-4599-5
Publication statusPublished - 2023
EventCLEO/Europe-EQEC 2023 - Munich, Germany
Duration: 26 Jun 202330 Jun 2023

Publication series

NameTechnical Digest Series
PublisherOptica Publishing Group


ConferenceCLEO/Europe-EQEC 2023


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