Coherent Ising machines with error correction feedback

TL;DR

This paper introduces an error correction feedback mechanism for coherent Ising machines, improving their ability to find correct solutions in combinatorial optimization by overcoming local minima and mapping issues.

Contribution

It proposes a novel error detection and correction feedback method that enhances the performance of coherent Ising machines in solving complex optimization problems.

Findings

Achieves efficient sampling of degenerate ground states

Reduces trapping in local minima

Improves mapping accuracy of the target Hamiltonian

Abstract

A non-equilibrium open-dissipative neural network, such as a coherent Ising machine based on mutually coupled optical parametric oscillators, has been proposed and demonstrated as a novel computing machine for hard combinatorial optimization problems. However, there are two challenges in the previously proposed approach: (1) The machine can be trapped by local minima which increases exponentially with problem size and (2) the machine fails to map a target Hamiltonian correctly on the loss landscape of a neural network due to oscillator amplitude heterogeneity. Both of them lead to erroneous solutions rather than correct answers. In this paper, we show that it is possible to overcome these two problems partially but simultaneously by introducing error detection and correction feedback mechanism. The proposed machine achieves efficient sampling of degenerate ground states and low-energy excited states via its inherent migration property during a solution search process.