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Machine Learning the Disorder Landscape of Majorana Nanowires
Jacob R Taylor1, Jay D Sau1, Sankar Das Sarma1
1Condensed Matter Theory Center and Joint Quantum Institute, Department of Physics, University of Maryland, College Park, Maryland 20742-4111, USA.
Physical Review Letters
|June 3, 2024
Summary
We developed a machine learning method to map disorder in Majorana nanowires using conductance data. This approach uniquely determines disorder details and Majorana properties from transport profiles.
Area of Science:
- Condensed Matter Physics
- Quantum Computing
- Materials Science
Background:
- Majorana nanowires are promising for topological quantum computing.
- Understanding disorder is crucial for their performance.
- Current methods for characterizing disorder are limited.
Purpose of the Study:
- To develop a practical machine learning approach for determining the disorder landscape in Majorana nanowires.
- To enable direct determination of topological invariants and Majorana wave-function structure.
- To facilitate optimization of Majorana systems by identifying underlying disorder.
Main Methods:
- Training a machine learning model on the conductance matrix.
- Inverting conductance data as a function of chemical potential and Zeeman energy.
- Utilizing different disorder parametrizations for unique inversion.
Main Results:
- The machine learning approach uniquely determines the disorder landscape from tunnel conductance.
- Accurate estimation of topological invariants and Majorana wave-function structure is achieved.
- The method allows for the determination of applicable spin-orbit coupling.
Conclusions:
- This work introduces a novel, practical machine learning technique for characterizing Majorana nanowires.
- The approach provides a direct link between transport profiles and fundamental topological properties.
- It opens new avenues for optimizing Majorana-based quantum devices through disorder analysis.
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