用无监督学习识别马约拉纳拓扑，仅凭数据就能区分拓扑与平凡相变点。
Unreasonable effectiveness of unsupervised learning in identifying Majorana topology
- 用自编码器结合有监督与无监督学习，从无标签数据中挖掘拓扑特征。
- 在短无序纳米线中成功区分拓扑与平凡相，并定位相变临界点。
- 适用于缺乏明确物理信号的拓扑材料，特别适合实验数据少的情况。
无监督学习不依赖标签数据，迫使算法从数据中自主发现隐藏模式，理论上可有效识别拓扑序——因其往往无明显物理表征（如拓扑超导）。然而，该方法计算开销大且难以收敛。本文通过自编码器融合有监督与无监督学习，证明在真实短无序纳米线的马约拉纳劈裂数据中，仅用无标签数据即可区分‘拓扑’与‘平凡’相，并精确定位参数空间中的相变点。该方法为在缺乏明确信号时识别马约拉纳纳米线拓扑提供了有力工具。
原文摘要 · Abstract (English)
In unsupervised learning, the training data for deep learning does not come with any labels, thus forcing the algorithm to discover hidden patterns in the data for discerning useful information. This, in principle, could be a powerful tool in identifying topological order since topology does not always manifest in obvious physical ways (e.g., topological superconductivity) for its decisive confirmation. The problem, however, is that unsupervised learning is a difficult challenge, necessitating huge computing resources, which may not always work. In the current work, we combine unsupervised and supervised learning using an autoencoder to establish that unlabeled data in the Majorana splitting in realistic short disordered nanowires may enable not only a distinction between `topological' and `trivial', but also where their crossover happens in the relevant parameter space. This may be a useful tool in identifying topology in Majorana nanowires.
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