MEDA: Measurement-Efficient Disorder-Aware Majorana Zero Mode Detection in Realistic Devices

2026-07-28

AI Summary

This work addresses the challenge of reliably distinguishing Majorana zero modes from trivial states in realistic devices, where strong disorder and finite-size effects undermine conventional topological invariants, while existing machine learning approaches suffer from limited scalability due to their reliance on dense conductance measurements. To overcome these limitations, the authors propose the MEDA framework, which— for the first time—directly maps sparse, experimentally accessible observables onto a physically interpretable periodic disorder invariant (PDI). By integrating a sparse measurement strategy with key features consistent with the topological gap protocol, MEDA achieves high-accuracy, noise-resilient detection of Majorana zero modes under moderate to strong disorder using only one-tenth of the measurement data required by traditional methods, thereby substantially alleviating the scalability bottleneck.

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.

Problem

• Majorana zero modes
• topological quantum computing
• disorder
• measurement efficiency
• topological indicators

Innovation

• Majorana zero modes
• disorder-aware detection
• measurement efficiency
• topological invariant
• sparse observables