Topological gap protocol based machine learning optimization of Majorana hybrid wires

TL;DR

This paper introduces a machine learning-based optimization method for Majorana hybrid wires, using a topological gap-inspired metric to reliably compensate for disorder and enhance topological phase stability.

Contribution

It presents a novel machine learning optimization approach utilizing a topological gap protocol-inspired metric for Majorana wires, improving disorder compensation.

Findings

Effective disorder compensation in Majorana wires

Reliable topological phase preservation

Enhanced device fabrication yield

Abstract

Majorana zero modes in superconductor-nanowire hybrid structures are a promising candidate for topologically protected qubits with the potential to be used in scalable structures. Currently, disorder in such Majorana wires is a major challenge, as it can destroy the topological phase and thus reduce the yield in the fabrication of Majorana devices. We study machine learning optimization of a gate array in proximity to a grounded Majorana wire, which allows us to reliably compensate even strong disorder. We propose a metric for optimization that is inspired by the topological gap protocol, and which can be implemented based on measurements of the non-local conductance through the wire.