Heuristic recurrent algorithms for photonic Ising machines

Charles Roques-Carmes (), Yichen Shen (), Cristian Zanoci, Mihika Prabhu, Fadi Atieh, Li Jing, Tena Dubček, Chenkai Mao, Miles R. Johnson, Vladimir Čeperić, John D. Joannopoulos, Dirk Englund and Marin Soljačić
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Charles Roques-Carmes: Massachusetts Institute of Technology
Yichen Shen: Massachusetts Institute of Technology
Cristian Zanoci: Massachusetts Institute of Technology
Mihika Prabhu: Massachusetts Institute of Technology
Fadi Atieh: Massachusetts Institute of Technology
Li Jing: Massachusetts Institute of Technology
Tena Dubček: Massachusetts Institute of Technology
Chenkai Mao: Massachusetts Institute of Technology
Miles R. Johnson: Massachusetts Institute of Technology
Vladimir Čeperić: Massachusetts Institute of Technology
John D. Joannopoulos: Massachusetts Institute of Technology
Dirk Englund: Massachusetts Institute of Technology
Marin Soljačić: Massachusetts Institute of Technology

Nature Communications, 2020, vol. 11, issue 1, 1-8

Abstract: Abstract The inability of conventional electronic architectures to efficiently solve large combinatorial problems motivates the development of novel computational hardware. There has been much effort toward developing application-specific hardware across many different fields of engineering, such as integrated circuits, memristors, and photonics. However, unleashing the potential of such architectures requires the development of algorithms which optimally exploit their fundamental properties. Here, we present the Photonic Recurrent Ising Sampler (PRIS), a heuristic method tailored for parallel architectures allowing fast and efficient sampling from distributions of arbitrary Ising problems. Since the PRIS relies on vector-to-fixed matrix multiplications, we suggest the implementation of the PRIS in photonic parallel networks, which realize these operations at an unprecedented speed. The PRIS provides sample solutions to the ground state of Ising models, by converging in probability to their associated Gibbs distribution. The PRIS also relies on intrinsic dynamic noise and eigenvalue dropout to find ground states more efficiently. Our work suggests speedups in heuristic methods via photonic implementations of the PRIS.

Date: 2020
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DOI: 10.1038/s41467-019-14096-z

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