Automatic detection of single-electron regime of quantum dots and definition of virtual gates using U-Net and clustering

TL;DR

This paper presents an automated method combining U-Net neural networks and clustering to detect single-electron regimes and define virtual gates in quantum dot charge stability diagrams, facilitating scalable quantum device control.

Contribution

It introduces an automated pipeline using U-Net and clustering for identifying charge transition lines and single-electron regimes, advancing quantum dot tuning automation.

Findings

Successfully detects charge transition lines in experimental data.

Accurately identifies single-electron regimes using clustering.

Automates the process for large-scale quantum device control.

Abstract

To realize practical quantum computers, a large number of quantum bits (qubits) will be required. Semiconductor spin qubits offer advantages such as high scalability and compatibility with existing semiconductor technologies. However, as the number of qubits increases, manual qubit tuning becomes infeasible, motivating automated tuning approaches. In this study, we use U-Net, a neural network method for object detection, to identify charge transition lines in experimental charge stability diagrams. The extracted charge transition lines are analyzed using the Hough transform to determine their positions and angles. Based on this analysis, we obtain the transformation matrix to virtual gates. Furthermore, we identify the single-electron regime by clustering the Hough transform outputs. We also show the single-electron regime within the virtual gate space. These sequential processes are performed automatically. This approach will advance automated control technologies for large-scale quantum devices.