论文标题
无序的拓扑图增强了非线性现象
Disordered topological graphs enhancing nonlinear phenomena
论文作者
论文摘要
复杂的网络在理解旋转，神经网络和电网的集体行为到疾病传播的现象中起着基本作用。最近已经利用了此类网络中的拓扑现象，以保留在存在障碍的情况下系统的响应。我们提出并展示具有模态结构的拓扑结构无序系统，该系统通过抑制从边缘模式到大型模式的能量的超快泄漏来增强拓扑通道中的非线性现象。我们介绍了图的构造，并表明其动力学通过数量级增强了受拓扑保护的光子对生成速率。无序的非线性拓扑图将使高级量子互连，有效的非线性来源以及用于人工智能的光基信息处理。
Complex networks play a fundamental role in understanding phenomena from the collective behavior of spins, neural networks, and power grids to the spread of diseases. Topological phenomena in such networks have recently been exploited to preserve the response of systems in the presence of disorder. We propose and demonstrate topological structurally disordered systems with a modal structure that enhances nonlinear phenomena in the topological channels by inhibiting the ultrafast leakage of energy from edge modes to bulk modes. We present the construction of the graph and show that its dynamics enhances the topologically protected photon pair generation rate by an order of magnitude. Disordered nonlinear topological graphs will enable advanced quantum interconnects, efficient nonlinear sources, and light-based information processing for artificial intelligence.
