Semiconductor quantum dots (QDs) are a leading platform for scalable quantum processors. However, scaling to large arrays requires reliable, automated tuning strategies for devices' bootstrapping, calibration, and operation, with many tuning aspects depending on accurately identifying QD device states from charge-stability diagrams (CSDs). In this work, we present a comprehensive benchmarking study of four modern machine learning (ML) architectures for multi-class state recognition in double-QD CSDs. We evaluate their performance across different data budgets and normalization schemes using both synthetic and experimental data. We find that the more resource-intensive models -- U-Nets and visual transformers (ViTs) -- achieve the highest MSE score (defined as $1-\mathrm{MSE}$) on synthetic data (over $0.98$) but fail to generalize to experimental data. MDNs are the most computationally efficient and exhibit highly stable training, but with substantially lower peak performance. CNNs offer the most favorable trade-off on experimental CSDs, achieving strong accuracy with two orders of magnitude fewer parameters than the U-Nets and ViTs. Normalization plays a nontrivial role: min-max scaling generally yields higher MSE scores but less stable convergence, whereas z-score normalization produces more predictable training dynamics but at reduced accuracy for most models. Overall, our study shows that CNNs with min-max normalization are a practical approach for QD CSDs.

翻译：半导体量子点（QDs）是可扩展量子处理器的主要平台之一。然而，扩展到大规模阵列需要可靠、自动化的器件引导、校准和操作调谐策略，其中许多调谐环节依赖于从电荷稳定图（CSDs）中准确识别量子点器件状态。本研究针对双量子点电荷稳定图的多类状态识别，对四种现代机器学习（ML）架构进行了全面的基准测试分析。我们利用合成数据与实验数据，评估了它们在不同数据规模和归一化方案下的性能。研究发现，资源需求较高的模型——U-Nets和视觉变换器（ViTs）——在合成数据上获得了最高的MSE分数（定义为$1-\mathrm{MSE}$，超过$0.98$），但未能推广到实验数据。混合密度网络（MDNs）计算效率最高且训练稳定性极佳，但峰值性能显著较低。卷积神经网络（CNNs）在实验电荷稳定图上实现了最有利的权衡，以比U-Nets和ViTs少两个数量级的参数量获得了较强的准确率。归一化处理具有关键影响：最小-最大缩放通常产生更高的MSE分数但收敛稳定性较差，而z分数归一化能带来更可预测的训练动态，但会降低大多数模型的准确率。总体而言，我们的研究表明，采用最小-最大归一化的卷积神经网络是处理量子点电荷稳定图的一种实用方法。