Experimental online quantum dots charge autotuning using neural networks

TL;DR

This paper demonstrates an experimental online charge autotuning method for quantum dots using neural networks, achieving high success rates and robustness, which advances scalable quantum computing control systems.

Contribution

It introduces a neural network-based autonomous charge tuning system for quantum dots, validated through real experiments, enabling scalable and reliable quantum device calibration.

Findings

Achieved 95% success rate in charge localization

Reduced measurement time through uncertainty-guided exploration

Validated robustness against noise and distribution shifts

Abstract

Spin-based semiconductor qubits hold promise for scalable quantum computing, yet they require reliable autonomous calibration procedures. This study presents an experimental demonstration of online single-dot charge autotuning using a convolutional neural network integrated into a closed-loop calibration system. The autotuning algorithm explores the gates' voltage space to localize charge transition lines, thereby isolating the one-electron regime without human intervention. This exploration leverages the model's uncertainty estimation to find the appropriate gate configuration with minimal measurements while reducing the risk of failures. In 20 experimental runs, our method achieved a success rate of 95% in locating the target electron regime, highlighting the robustness of this approach against noise and distribution shifts from the offline training set. Each tuning run lasted an average of 2 hours and 9 minutes, primarily due to the limited speed of the current measurement. This work validates the feasibility of machine learning-driven real-time charge autotuning for quantum dot devices, advancing the development toward the control of large qubit arrays.