发表机构
School of Electrical Engineering and Telecommunications, University of New South Wales, Sydney, New South Wales, Australia（电子工程与电信学系，新南威尔士大学，悉尼，新南威尔士，澳大利亚）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对孤立双量子点电荷态表征，提出两个参数少的卷积神经网络，经训练可识别电荷不稳定性、定位跃迁线等，合成图像预训练提高标注效率，模型在标准硬件上表现良好，为量子点器件表征和调谐提供实用路径。
AI 中文摘要
将半导体量子点阵列扩展到容错量子计算需要对自旋量子比特进行有效调谐，这一过程依赖于电荷稳定性图（CSM）分析且很大程度上仍是手动操作。机器学习虽已广泛应用于库耦合器件的CSM分析，但在日益重要的孤立模式下的自动调谐却关注有限。在孤立模式CSM中，电荷跃迁呈近垂直线，适合紧凑的特定任务模型。我们提出两个参数少于百万的卷积神经网络，在约1K温度下用自动低温探测系统从32个硅金属氧化物半导体（SiMOS）双量子点器件收集的CSM上训练。16个器件用于训练，16个用于评估跨器件泛化。CSMClassifier识别电荷不稳定性和传感器伪像，ChargeLineNet定位电荷跃迁线并确定电子占据情况。合成图像预训练大幅提高标注效率，微调预训练模型在有限实验数据上保持超90%准确率，从头训练则显著下降。两个模型仅占6.5MB，在标准实验室硬件上处理图像不到60毫秒，为量子点器件的可扩展、自动表征和调谐提供了实用途径。
英文摘要
Scaling semiconductor quantum dot arrays toward fault-tolerant quantum computing requires efficient tuneup of spin qubits, a process that depends on the analysis of charge stability maps (CSMs) and remains largely manual. While machine learning has been widely applied to CSM analysis in reservoir-coupled devices, automated tuning in the increasingly important isolated-mode regime has received limited attention. In isolated-mode CSMs, charge transitions appear as near-vertical lines, making them well suited to compact, task-specific models. We present two convolutional neural networks with fewer than one million parameters, trained on CSMs collected from 32 silicon metal-oxide-semiconductor (SiMOS) double-quantum-dot devices measured at approximately 1 K using an automated cryogenic probing system. Sixteen devices were used for training and sixteen were held out to evaluate cross-device generalization against hand-labeled ground truth. CSMClassifier identifies charge instability and sensor artifacts, achieving 94% macro-averaged accuracy across three quality classes on 2,407 held-out images. ChargeLineNet localizes charge-transition lines and determines electron occupancy, achieving 95.3% exact line-count accuracy on 1,131 held-out images. Combined into a single pipeline, the models correctly determine electron occupancy for 93.8% of clean held-out images. Pre-training on synthetic images substantially improves label efficiency. Fine-tuning the pre-trained model on limited experimental data maintains over 90% accuracy, whereas training from scratch degrades significantly under the same conditions. Together, the two models occupy only 6.5 MB and process images in less than 60 ms on standard laboratory hardware, demonstrating a practical path toward scalable, automated characterization and tuneup of quantum-dot devices.
