Avoiding Dilution: Using Diffusion and Vision Transformers to resolve Majorana Features in Nanowires at High Temperature
arXiv:2607.14949
The paper shows that neural networks based on diffusion-inspired U‑Net transformers and vision transformers can reconstruct low‑temperature conductance and predict topological visibility of Majorana nanowires from easier high‑temperature measurements, enabling early screening of devices.
Abstract
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 for local conductance and 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 . 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.
3 Figures, 6 Pages