Machine learning optimization of Majorana hybrid nanowires

Abstract

As the complexity of quantum systems such as quantum bit arrays increases, efforts to automate expensive tuning are increasingly worthwhile. We investigate machine learning based tuning of gate arrays using the CMA-ES algorithm for the case study of Majorana wires with strong disorder. We find that the algorithm is able to efficiently improve the topological signatures, learn intrinsic disorder profiles, and completely eliminate disorder effects. For example, with only 20 gates, it is possible to fully recover Majorana zero modes destroyed by disorder by optimizing gate voltages.

13 pages, 13 figures; added references

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Cited by in corpus (10)

• Theory of Majorana zero modes in unconventional superconductors
• Mitigating disorder-induced zero-energy states in weakly-coupled semiconductor-superconductor hybrid systems
• Entanglement measures of Majorana bound states
• Machine-learned tuning of artificial Kitaev chains from tunneling-spectroscopy measurements
• Machine Learning the Disorder Landscape of Majorana Nanowires
• Distributed Evolution Strategies with Multi-Level Learning for Large-Scale Black-Box Optimization
• Vision transformer based Deep Learning of Topological indicators in Majorana Nanowires
• Topological gap protocol based machine learning optimization of Majorana hybrid wires
• Automated in situ optimization and disorder mitigation in a quantum device
• Mitigating disorder and optimizing topological indicators with vision-transformer-based neural networks in Majorana nanowires