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Experimental Online Quantum Dots Charge Autotuning Using Neural Network
- Publication Year :
- 2024
-
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. In 20 experimental runs on a device cooled to 25mK, the method achieved a success rate of 95% in locating the target electron regime, highlighting the robustness of this method 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.<br />Comment: 6 pages (main) + 5 pages (supplementary)
Details
- Database :
- arXiv
- Publication Type :
- Report
- Accession number :
- edsarx.2409.20320
- Document Type :
- Working Paper