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Majorana zero modes in superconductor-nanowire hybrid structures are apromising candidate for topologically protected qubits with the potential to beused in scalable structures. Currently, disorder in such Majorana wires is amajor challenge, as it can destroy the topological phase and thus reduce theyield in the fabrication of Majorana devices. We study machine learningoptimization of a gate array in proximity to a grounded Majorana wire, whichallows us to reliably compensate even strong disorder. We propose a metric foroptimization that is inspired by the topological gap protocol, and which can beimplemented based on measurements of the non-local conductance through thewire.

Topological gap protocol based machine learning optimization of Majorana hybrid wires