Machine Learning the Disorder Landscape of Majorana Nanowires

Abstract

We develop a practical machine learning approach to determine the disorder landscape of Majorana nanowires by using training of the conductance matrix and inverting the conductance data in order to obtain the disorder details in the system. The inversion carried out through machine learning using different disorder parametrizations turns out to be unique in the sense that any input tunnel conductance as a function of chemical potential and Zeeman energy can indeed be inverted to provide the correct disorder landscape. Our work opens up a qualitatively new direction of directly determining the topological invariant and the Majorana wave-function structure corresponding to a transport profile of a device using simulations that quantitatively match the specific conductance profile. In addition, this also opens up the possibility for optimizing Majorana systems by figuring out the (generally unknown) underlying disorder only through the conductance data. An accurate estimate of the applicable spin-orbit coupling in the system can also be obtained within the same scheme.

13 pages, 29 figures

References in corpus (9)

• Majorana Fermions and a Topological Phase Transition in Semiconductor-Superconductor Heterostructures
• InAs-Al Hybrid Devices Passing the Topological Gap Protocol
• Disorder-induced zero-bias peaks in Majorana nanowires
• Estimating disorder and its adverse effects in semiconductor Majorana nanowires
• In search of Majorana
• Spectral properties, topological patches, and effective phase diagrams of finite disordered Majorana nanowires
• Density of states, transport, and topology in disordered Majorana nanowires
• Machine learning optimization of Majorana hybrid nanowires
• Reconstructing the potential configuration in a high-mobility semiconductor heterostructure with scanning gate microscopy

Cited by in corpus (10)

• Rabi and Ramsey oscillations of a Majorana qubit in a quantum dot-superconductor array
• Neural network based deep learning analysis of semiconductor quantum dot qubits for automated control
• Disordered Majorana nanowires: Studying disorder without any disorder
• Josephson effect and critical currents in trivial and topological full-shell hybrid nanowires
• Vision transformer based Deep Learning of Topological indicators in Majorana Nanowires
• Majorana zero modes in semiconductor-superconductor hybrid structures: Defining topology in short and disordered nanowires through Majorana splitting
• Mitigating disorder and optimizing topological indicators with vision-transformer-based neural networks in Majorana nanowires
• AI-enhanced tuning of quantum dot Hamiltonians toward Majorana modes
• Rashba spin-orbit coupling and artificially engineered topological superconductors
• Ballistic transport in 1D Rashba systems in the context of Majorana nanowires