Topological gap protocol based machine learning optimization of Majorana hybrid wires
This work addresses a critical challenge in fabricating Majorana-based qubits for quantum computing, though it appears incremental as it builds on existing topological gap protocols.
The authors tackled the problem of disorder in Majorana hybrid wires, which destroys the topological phase and reduces device yield, by using machine learning to optimize a gate array to compensate for strong disorder, achieving reliable compensation as a result.
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.