Topological gap protocol based machine learning optimization of Majorana hybrid wires
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Abstract (arXiv)
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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References · 8
- 2016The CMA Evolution Strategy: A Tutorialcited by 364
- 2016CMA-ES for Hyperparameter Optimization of Deep Neural Networkscited by 47
- 2020Machine learning enables completely automatic tuning of a quantum device faster than human expertscited by 23
- 2020Quantum device fine-tuning using unsupervised embedding learningcited by 14
- 2018Efficiently measuring a quantum device using machine learningcited by 9
- 2021Bridging the reality gap in quantum devices with physics-aware machine learningcited by 6
- 2022Learning Coulomb Diamonds in Large Quantum Dot Arrayscited by 2
- 2022Machine learning optimization of Majorana hybrid nanowirescited by 1
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