基于神经网络的实验性在线量子点电荷自动调谐
Experimental online quantum dots charge autotuning using neural networks
AI总结：
本研究利用卷积神经网络实现量子点电荷自动调谐，通过不确定性估计引导栅极电压探索，以95%成功率定位单电子区域，验证了机器学习驱动的实时调谐可行性。
摘要：
基于自旋的半导体量子比特有望实现可扩展的量子计算，但它们需要可靠的自主校准程序。本研究展示了一种在线单点电荷自动调谐的实验演示，该调谐使用集成在闭环校准系统中的卷积神经网络。自动调谐算法探索栅极电压空间以定位电荷跃迁线，从而在无需人工干预的情况下隔离单电子区域。这种探索利用模型的不确定性估计来以最少的测量次数找到合适的栅极配置，同时降低失败风险。在20次实验运行中，我们的方法在定位目标电子区域方面达到了95%的成功率，突显了该方法对噪声和离线训练集分布偏移的鲁棒性。每次调谐运行平均耗时2小时9分钟，主要受限于当前测量的速度。这项工作验证了机器学习驱动的实时电荷自动调谐对量子点器件的可行性，推进了向大型量子比特阵列控制的发展。
英文摘要：
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.
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