MEDA: Measurement-Efficient Disorder-Aware Majorana Zero Mode Detection in Realistic Devices
arXiv:2607.26208
The paper introduces MEDA, a framework that uses sparse, experimentally accessible measurements to detect Majorana zero modes in disordered quantum devices by mapping them to a robust periodic disorder invariant, achieving a tenfold reduction in measurement effort.
Abstract
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.
Accepted to 2026 IEEE International Conference on Quantum Computing and Engineering