arXiv · 2305.16230
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.
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Matthias Thamm, Bernd Rosenow. 2023-05-25. Topological gap protocol based machine learning optimization of Majorana hybrid wires. https://doi.org/10.1103/physrevb.109.045132
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