Experimental Online Quantum Dots Charge Autotuning Using Neural Networks.

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

SUBMITTER: Yon V 

PROVIDER: S-EPMC11907638 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature