Topological gap protocol based machine learning optimization of Majorana hybrid wires
Fuente:
arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2023
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| _version_ | 1866916097338376192 |
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| author | Thamm, Matthias Rosenow, Bernd |
| author_facet | Thamm, Matthias Rosenow, Bernd |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2305_16230 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Topological gap protocol based machine learning optimization of Majorana hybrid wires Thamm, Matthias Rosenow, Bernd Mesoscale and Nanoscale Physics Machine Learning 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. |
| title | Topological gap protocol based machine learning optimization of Majorana hybrid wires |
| topic | Mesoscale and Nanoscale Physics Machine Learning |
| url | https://arxiv.org/abs/2305.16230 |