AI 中文总结
该研究针对真实器件中 MZM 检测的拓扑偏差与测量瓶颈，提出 MEDA 框架，可将测量量减少 10 倍并保持预测质量，且具有强物理可解释性。
AI 中文摘要
容错拓扑量子计算依赖于 Majorana 零模（MZMs）的识别，但在真实器件中实现可靠检测仍具挑战性。传统拓扑指标在有限、无序系统中存在固有偏差，模糊了真实 MZMs 与平凡态的区分。此外，通过机器学习将这些指标映射到真实可观测量的尝试需要密集、昂贵的电导测量，造成严重的扩展性瓶颈。为同时解决拓扑偏差和测量限制问题，我们提出 MEDA：一种面向真实器件的测量高效且 Disorder 感知的 MZM 检测框架。MEDA 将稀疏、实际可获取的可观测量直接映射到鲁棒周期性 Disorder 不变量（PDI）。利用新颖的稀疏参数机制，MEDA 减少了 10 倍的测量量，同时保持预测质量，甚至在限制传统方法的中等到强无序 regime 中也能适用。此外，MEDA 自然优先考虑与拓扑间隙协议一致的输入特征，展现出强大的物理可解释性。
英文摘要
Fault-tolerant topological quantum computing relies on identifying Majorana zero modes (MZMs), but reliable detection in realistic devices remains challenging. Conventional topological indicators are inherently biased in finite, disordered systems, blurring the distinction between true MZMs and trivial states. Furthermore, attempts to map these indicators to real observables via machine learning require dense, expensive conductance measurements, creating a severe scaling bottleneck. To simultaneously address topological bias and measurement limitations, we present MEDA: a Measurement-Efficient, Disorder-Aware framework for MZM detection in realistic devices. MEDA maps sparse, practically obtainable observables directly to the robust periodic disorder invariant (PDI). Using a novel sparse parameter regime, MEDA reduces measurement volume by 10x while maintaining predictive quality, even in moderate to strong disorder regimes that limit conventional methods. Furthermore, MEDA naturally prioritizes input features consistent with the topological gap protocol, demonstrating strong physical interpretability.
CommentsAccepted to 2026 IEEE International Conference on Quantum Computing and Engineering
