论文标题
具有错误校正反馈的连贯的ISING机器
Coherent Ising machines with error correction feedback
论文作者
论文摘要
已经提出并证明了一个基于相互耦合的光学参数振荡器的非平衡开放性神经网络，例如基于相互耦合的光学参数振荡器的连贯性iSing机器，并作为用于硬组合优化问题的新型计算机。但是，在先前提出的方法中有两个挑战：（1）机器可以被当地的最小值捕获，该局部最小值随着问题大小而呈指数增加，并且（2）机器由于振荡器网络的损失景观而无法正确映射目标汉密尔顿，这是由于振荡器网络的损失景观，这是由于振荡器网络的损失景观。他们俩都导致错误的解决方案，而不是正确的答案。在本文中，我们表明可以通过引入错误检测和校正反馈机制来部分但同时克服这两个问题。所提出的机器通过在解决方案搜索过程中通过其固有的迁移特性来实现退化基态和低能激发态的有效抽样。
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.
