应用于量子点电荷状态检测的监督机器学习中的合成和实验训练数据的评估
Evaluation of synthetic and experimental training data in supervised machine learning applied to charge state detection of quantum dots
摘要：
门定义的量子点的自动调谐是大规模基于半导体的量子位初始化的要求。这些调整程序的一个重要步骤是基于电荷稳定图的电荷状态检测。使用监督机器学习来执行此任务需要一个大型数据集用于模型训练。为了避免手工标记实验数据，已探索合成数据作为替代方案。虽然与使用实验数据相比，训练数据集的大小显着增加，但使用合成数据意味着分类器是在来自与作为调整过程的一部分的实验数据不同的分布的数据上训练的。在这里，我们评估了一系列在模拟和实验数据上训练的机器学习模型的预测准确性，以及它们在二维电子气和纳米线器件中推广实验电荷稳定性图的能力。我们发现分类器在纯实验或合成和实验训练数据的组合上表现最好，并且将常见的实验噪声签名添加到合成数据并不能显著提高分类精度。这些结果表明，实验训练数据以及现实的量子点模拟和噪声模型在使用监督机器学习的电荷状态检测中是必不可少的。
英文摘要：
Automated tuning of gate-defined quantum dots is a requirement for large scale semiconductor based qubit initialisation. An essential step of these tuning procedures is charge state detection based on charge stability diagrams. Using supervised machine learning to perform this task requires a large dataset for models to train on. In order to avoid hand labelling experimental data, synthetic data has been explored as an alternative. While providing a significant increase in the size of the training dataset compared to using experimental data, using synthetic data means that classifiers are trained on data sourced from a different distribution than the experimental data that is part of the tuning process. Here we evaluate the prediction accuracy of a range of machine learning models trained on simulated and experimental data and their ability to generalise to experimental charge stability diagrams in two dimensional electron gas and nanowire devices. We find that classifiers perform best on either purely experimental or a combination of synthetic and experimental training data, and that adding common experimental noise signatures to the synthetic data does not dramatically improve the classification accuracy. These results suggest that experimental training data as well as realistic quantum dot simulations and noise models are essential in charge state detection using supervised machine learning.
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