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.

中文翻译：

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具有纠错反馈的相干伊辛机

非平衡开放耗散神经网络，如基于相互耦合的光学参量振荡器的相干伊辛机，已被提出并证明是一种用于解决硬组合优化问题的新型计算机。然而，先前提出的方法存在两个挑战：（1）机器可能被局部最小值所困，局部最小值随问题规模呈指数增长；（2）机器无法在神经网络的损失范围内正确映射目标哈密顿量由于振荡器振幅异质性。它们都会导致错误的解决方案而不是正确的答案。在本文中，我们表明通过引入错误检测和纠正反馈机制可以部分但同时地克服这两个问题。