Machine learning for Majorana Nanowires

Majorana nanowires, hybrid superconductor–semiconductor systems capable of supporting non-Abelian Majorana zero modes (MZMs)—are a leading platform for the pursuit of topological quantum computation. However, understanding and controlling their behavior remain challenging due to the interplay of disorder, finite-size effects, and complex experimental signatures that often obscure the underlying topological physics. Recent progress in machine learning (ML) offers new pathways for characterizing, predicting, and optimizing the properties of such systems. In this work, we develop and apply ML-based frameworks to analyze simulated and experimentally relevant data from Majorana nanowires.