arXiv cs.AI· Benquan Wang, Trishita Das, Yuhan Peng, Tatjana Kleine, Shanshan Chang, Jinhui Chen, Nilo Mata-Cervera, Chunyu Li, Kelin Xia, Andrew Forbes, Yijie Shen·· 6 小时前AI 评分31
TOPO²：用拓扑学习从光场中恢复拓扑信息
Recovering topological information of light by topological learning
AI 导读
研究者提出 TOPO²，一个利用拓扑学习从光波中恢复拓扑信息的人工智能框架。该框架可在强无序畸变的光场中，仅用普通相机单次提取嵌入 Skyrmion 拓扑的拓扑数。基于 TOPO² 的通信方案能发送并恢复以拓扑字母表编码的信息，性能优于传统方法，为鲁棒高容量信息编码开辟拓扑信息光学新路径。
正文 · 原文
Authors:Benquan Wang, Trishita Das, Yuhan Peng, Tatjana Kleine, Shanshan Chang, Jinhui Chen, Nilo Mata-Cervera, Chunyu Li, Kelin Xia, Andrew Forbes, Yijie Shen
Abstract:The evolution of modern-day communication networks towards enhanced capacity and robustness is driving interest in topological light wave as a next-generation information carrier. However, harnessing its potential is hindered by the lack of an effective topological reader, particularly when strong disorder renders conventional readout intractable. Here we address this by introducing TOPO$^{2}$, an artificial intelligence framework that leverages on topological learning to recover topological information from light. We demonstrate it on highly distorted optical fields with embedded Skyrmion topologies, showing that we can extract the topological numbers in a single shot with just a conventional camera. We showcase its capability in a TOPO$^{2}$-based communication scheme, sending and recovering information encoded in a topological alphabet, confirming that it outperforms conventional approaches. Our connection of topological photonics with topological AI as a powerful paradigm for robust high-capacity information encoding and processing opens a new path towards topological information optics.
| Comments: | 5 figures |
| Subjects: | Optics (physics.optics); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2609.06542 [physics.optics] |
| (or arXiv:2609.06542v2 [physics.optics] for this version) | |
| https://doi.org/10.48550/arXiv.2609.06542 arXiv-issued DOI via DataCite |
Submission history
From: Benquan Wang [view email]
[v1]
Sun, 6 Sep 2026 11:30:11 UTC (4,598 KB)
[v2]
Mon, 26 Oct 2026 15:57:01 UTC (4,834 KB)
来源：arXiv cs.AI · arxiv.org