arXiv subjects

Dominic Leclerc

Publications and source records attributed to Dominic Leclerc.

2 recordsLinked to original sources

arXiv2025

Rapid Autotuning of a SiGe Quantum Dot into the Single-Electron Regime with Machine Learning and RF-Reflectometry FPGA-Based Measurements

Spin qubits need to operate within a very precise voltage space around charge state transitions to achieve high-fidelity gates. However, the stability diagrams that allow the identification of the desired charge states are long to acquire. Moreover, the voltage space to search for the desired charge state increases quickly with the number of qubits. Therefore, faster stability diagram acquisitions are needed to scale up a spin qubit quantum processor. Currently, most methods focus on more efficient data sampling. Our approach shows a significant speedup by combining measurement speedup and a reduction in the number of measurements needed to tune a quantum dot device. Using an autotuning algorithm based on a neural network and faster measurements by harnessing the FPGA embedded in Keysight's Quantum Engineering Toolkit (QET), the measurement time of stability diagrams has been reduced by a factor of 9.8. This led to an acceleration factor of 2.2 for the total initialization time of a SiGe quantum dot into the single-electron regime, which is limited by the Python code execution.

cond-mat.mes-hall↗

arXiv2024

Experimental online quantum dots charge autotuning using neural networks

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.

cond-mat.mes-hall↗