@article{21640,
  abstract     = {Conventional computing architectures have no known efficient algorithms for combinatorial optimization tasks such
as the Ising problem, which requires finding the ground state spin configuration of an arbitrary Ising graph. Physical
Ising machines have recently been developed as an alternative to conventional exact and heuristic solvers; however,
these machines typically suffer from decreased ground state convergence probability or universality for high edge-
density graphs or arbitrary graph weights, respectively. We experimentally demonstrate a proof-of-principle integrated
nanophotonic recurrent Ising sampler (INPRIS), using a hybrid scheme combining electronics and silicon-on-insulator
photonics, that is capable of converging to the ground state of various four-spin graphs with high probability. The
INPRIS results indicate that noise may be used as a resource to speed up the ground state search and to explore larger
regions of the phase space, thus allowing one to probe noise-dependent physical observables. Since the recurrent pho-
tonic transformation that our machine imparts is a fixed function of the graph problem and therefore compatible with
optoelectronic architectures that support GHz clock rates (such as passive or non-volatile photonic circuits that do not
require reprogramming at each iteration), this work suggests the potential for future systems that could achieve orders-
of-magnitude speedups in exploring the solution space of combinatorially hard problems. },
  author       = {Prabhu, Mihika and Roques-Carmes, Charles and Shen, Yichen and Harris, Nicholas and Jing, Li and Carolan, Jacques and Hamerly, Ryan and Baehr-Jones, Tom and Hochberg, Michael and Čeperić, Vladimir and Joannopoulos, John D. and Englund, Dirk R. and Soljačić, Marin},
  issn         = {2334-2536},
  journal      = {Optica},
  number       = {5},
  pages        = {551--558},
  publisher    = {Optica Publishing Group},
  title        = {{Accelerating recurrent Ising machines in photonic integrated circuits}},
  doi          = {10.1364/optica.386613},
  volume       = {7},
  year         = {2020},
}