通过拓扑学习恢复光的拓扑信息
Recovering topological information of light by topological learning
- Nanyang Technological University（南洋理工大学）
- University of the Witwatersrand（威特沃特斯兰德大学）
- Xiamen University（厦门大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对强无序信道中光拓扑信息丢失的问题，提出拓扑增强AI方法TOPO$^{2}$，利用多尺度拓扑不变量从散斑中高效识别拓扑态，仅需单次强度图案，优于标准算法，并实现图像重建，为鲁棒通信开辟新路。
AI中文摘要：
现代通信网络向具有更高容量和鲁棒性的光学解决方案的演进，正推动人们对拓扑光波的兴趣，拓扑光波利用拓扑不变量（如斯格明子数）来抵抗扰动。然而，即使在理想条件下，检测底层拓扑仍然是一个计算密集的过程，在通过强无序信道后变得难以处理，此时退化为不可识别的散斑似乎破坏了拓扑。在这里，我们提出并展示了一种拓扑增强的人工智能（AI）方法，通过在多个长度尺度上计算性地利用数据中的拓扑不变量，来恢复和分类这种看似丢失的拓扑信息。通过将信息的拓扑分类与光的拓扑对齐，我们的拓扑增强学习协议（称为TOPO$^{2}$）实现了对光拓扑状态的高效识别，即使从散斑中也能识别，且无需任何先验学习。我们的方法在基准测试中优于标准计算算法，并且具有仅需单个强度图案作为输入的优点，便于单次操作。为了演示这一点，我们利用斯格明子数作为通过无序信道传输图像的鲁棒数据载体，使用TOPO$^{2}$准确重建传输的图像。这项工作将拓扑光子学与拓扑AI协同起来，以揭示光中隐藏的拓扑特征，为即使在极端无序环境中实现鲁棒通信开辟了道路。
英文摘要：
The evolution of modern-day communication networks towards optical solutions with enhanced capacity and robustness is driving interest in topological light waves, exploiting their stability against perturbations through a topological invariant, e.g., the skyrmion number. However, detecting the underlying topology remains a computationally intense process even under ideal conditions, becoming intractable after passing through strongly disordered channels, where the degradation into unrecognisable speckle appears to destroy the topology. Here, we propose and demonstrate a topology-enhanced artificial intelligence (AI) approach to recover and classify such apparently lost topological information by computationally leveraging topological invariants in the data across many length scales. By aligning the topological classification of information with the topology of light, our topology-enhanced learning protocol, termed TOPO$^{2}$, achieves highly efficient recognition of the topological states of light, even from speckle, without the need for any prior learning. Our approach outperforms benchmark tests against standard computational algorithms and has the benefit of requiring just a single intensity pattern as the input, facilitating single-shot operation. To demonstrate this, we leverage the skyrmion number as a robust data carrier of images through a disordered channel, using TOPO$^{2}$ to accurately reconstruct the transmitted images. This work synergises topological photonics and topological AI for unravelling hidden topological signatures in light, opening a pathway towards robust communications even in extreme disordered environments.
