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IEICE Technical Report; IEICE Tech. Rep.

Optical oscillator network for solving combinatorial optimization problems -- Coherent Ising machine

Shoko Utsunomiya, Shuhei Tamate, Yoshitaka Haribara, Sakaguchi Hiromasa, Hiroki Takesue, Takahiro Inagaki, Kensuke Inaba, Kyo Inoue, Y. Yamamoto

Published Jun 6, 2016• 0 citations
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We introduce the recent progress of coherent Ising machine (CIM), which finds solutions to complex combinatorial optimization problems using a network of degenerate optical parametric oscillators (DOPO). We describe our effort to generate time-multiplexed DOPOs using long-distance fiber cavities including telecom-band phase sensitive amplifiers [1-4]. We then present our experiments where we found solutions to complex combinatorial optimization problems using a CIM with measurement-feedback scheme [5]. Finally, a future perspective of the CIM project will briefly described.
Recently, the coherent Ising machine (CIM) as a degenerate optical parametric oscillator (DOPO) network has been researched to solve Ising combinatorial optimization problems. We formulate a theoretical model for the CIM with discrete-time measurement feedback processes, and perform numerical simulations for the simplest network, composed of two degenerate optical parametric oscillator pulses with the antiferromagnetic mutual coupling. We evaluate the extent to which quantum coherence exists during the optimization process.
Coherent Ising machines (CIMs) are a promising approach for quantum computation. It exhibits excellent per-formance in solving the combinatorial optimization problems, however, the existence of amplitude heterogeneity limits the applications of CIMs. Here in this work, we present a system of the spiking neural network composed of antisymmetrically coupled degenerate optical parametric oscillator pulses and dissipative pulses. It works based on the principles of CIM during solving the combinatorial optimization problems. Moreover, a nonlinear transfer function is chosen to mitigate the amplitude inhomogeneities and destabilize the resulting local minima ac-cording to the dynamical behavior of spiking neurons.