Topological gap protocol based machine learning optimization of Majorana hybrid wires

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.

13 pages, 11 figures

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Cited by in corpus (5)

• Theory of Majorana zero modes in unconventional superconductors
• Spin-resolved nonlocal transport in proximitized Rashba nanowires
• Automated in situ optimization and disorder mitigation in a quantum device
• AI-enhanced tuning of quantum dot Hamiltonians toward Majorana modes
• Mitigating disorder and optimizing topological indicators with vision-transformer-based neural networks in Majorana nanowires