论文标题
非线性参数振荡器可以求解随机ISING模型吗？
Can nonlinear parametric oscillators solve random Ising models?
论文作者
论文摘要
我们将大型参数振荡器的网络作为随机ISING模型的启发式求解器。在这些网络（称为连贯的Ising机器）中，要求解的模型在振荡器之间的耦合中编码，并且网络的稳态状态提供了解决方案。这种方法依赖于模式竞争将网络引导到ISING模型的基态解决方案的假设。通过考虑一个广泛的沮丧的伊辛模型，我们表明最有效的模式与Ising模型的基态无关。我们推断，接近阈值的参数振荡器网络本质上不是求解器。然而，如果在非线性发挥主要作用的状态下，如果振荡器足够高于阈值，则网络可以找到正确的解决方案。我们发现，对于模型的所有探测实例，网络都以有限的概率收敛到Ising模型的基态。
We study large networks of parametric oscillators as heuristic solvers of random Ising models. In these networks, known as coherent Ising machines, the model to be solved is encoded in the coupling between the oscillators, and a solution is offered by the steady state of the network. This approach relies on the assumption that mode competition steers the network to the ground-state solution of the Ising model. By considering a broad family of frustrated Ising models, we show that the most-efficient mode does not correspond generically to the ground state of the Ising model. We infer that networks of parametric oscillators close to threshold are intrinsically not Ising solvers. Nevertheless, the network can find the correct solution if the oscillators are driven sufficiently above threshold, in a regime where nonlinearities play a predominant role. We find that for all probed instances of the model, the network converges to the ground state of the Ising model with a finite probability.
