避免稀释：利用扩散和视觉变压器解析高温下纳米线中的马约拉纳特征
Avoiding Dilution: Using Diffusion and Vision Transformers to resolve Majorana Features in Nanowires at High Temperature
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中文总结 AI 辅助
研究利用扩散和视觉变压器，通过生成高低温电导模拟并训练神经网络，从高温电导数据恢复和推断低温马约拉纳纳米线特征，为早期筛选不良器件提供实用途径，加速马约拉纳纳米线器件开发的实验反馈循环。
中文摘要 AI 辅助
识别半导体 - 超导体纳米线中的马约拉纳零模需要在稀释制冷机中进行超低温输运测量，这使得器件筛选缓慢且资源密集。本文研究高温电导数据能否用于在将器件投入稀释制冷机表征之前推断低温马约拉纳纳米线特性。生成无序马约拉纳纳米线的高低温电导模拟并训练神经网络执行两项相关任务。一是用受扩散启发的移位窗口U-Net变压器架构从热展宽的高温测量中重建低温电导，局部电导 \(R^2\approx0.95\)，非局部电导 \(R^2\approx0.91\)；二是训练基于视频视觉变压器的网络直接从高温电导预测低温拓扑可见性，\(R^2\approx0.80\)。结果表明机器学习模型可从高温数据恢复和推断低温马约拉纳特征，为早期剔除不良器件提供实用途径，加速马约拉纳纳米线器件开发的实验反馈循环。
英文摘要
Identifying Majorana zero modes in semiconductor--superconductor nanowires requires ultra-low temperature transport measurements in dilution refrigerators, making device screening slow and resource-intensive. Here, we investigate whether high-temperature conductance data can be used to infer low-temperature Majorana nanowire properties before committing devices to dilution-refrigerator characterization. We generate paired high- and low-temperature conductance simulations for disordered Majorana nanowires and train neural networks to perform two related tasks. First, we use a Shifted Window U-Net Transformer diffusion-inspired architecture to reconstruct low-temperature conductance from thermally broadened high-temperature measurements, achieving high-fidelity recovery with $R^2 \approx {0.95}$ for local conductance and $R^2 \approx {0.91}$ for nonlocal conductance. Second, we train a Video Vision Transformer-based network to predict the low-temperature topological visibility directly from high-temperature conductance, obtaining $R^2 \approx {0.80}$. These results demonstrate that machine-learning models can recover and infer low-temperature Majorana features from experimentally easier high-temperature data, providing a practical route for rejecting poor devices early thus avoiding slow and resource-intensive dilution refrigeration for non-promising devices. This high-temperature screening approach could substantially accelerate the experimental feedback loop for Majorana nanowire device development.
