Combinatorial optimization solving by coherent Ising machines based on spiking neural networks

TL;DR

This paper presents an optical spiking neural network based on coherent Ising machines, demonstrating its potential to accelerate combinatorial optimization problems through neuromorphic optical computing.

Contribution

It introduces a novel optical spiking neural network architecture utilizing degenerate optical parametric oscillators for improved optimization performance.

Findings

The network accelerates combinatorial optimization tasks.

It stabilizes local minima via nonlinear transfer functions.

Demonstrates promising results for neural and optical computing applications.

Abstract

Spiking neural network is a kind of neuromorphic computing that is believed to improve the level of intelligence and provide advantages for quantum computing. In this work, we address this issue by designing an optical spiking neural network and find that it can be used to accelerate the speed of computation, especially on combinatorial optimization problems. Here the spiking neural network is constructed by the antisymmetrically coupled degenerate optical parametric oscillator pulses and dissipative pulses. A nonlinear transfer function is chosen to mitigate amplitude inhomogeneities and destabilize the resulting local minima according to the dynamical behavior of spiking neurons. It is numerically shown that the spiking neural network-coherent Ising machines have excellent performance on combinatorial optimization problems, which is expected to offer new applications for neural computing and optical computing.