The rapid development of quantum dot (QD) devices for quantum computing has necessitated more efficient and automated methods for device characterization and tuning. Many of the measurements acquired during the tuning process come in the form of images that need to be properly analyzed to guide the subsequent tuning steps. By design, features present in such images capture certain behaviors or states of the measured QD devices. When considered carefully, such features can aid the control and calibration of QD devices. An important example of such images are so-called \textit{triangle plots}, which visually represent current flow and reveal characteristics important for QD device calibration. While image-based classification tools, such as convolutional neural networks (CNNs), can be used to verify whether a given measurement is \textit{good} and thus warrants the initiation of the next phase of tuning, they do not provide any insights into how the device should be adjusted in the case of \textit{bad} images. This is because CNNs sacrifice prediction and model intelligibility for high accuracy. To ameliorate this trade-off, a recent study introduced an image vectorization approach that relies on the Gabor wavelet transform [1]. Here we propose an alternative vectorization method that involves mathematical modeling of synthetic triangles to mimic the experimental data. Using explainable boosting machines, we show that this new method offers superior explainability of model prediction without sacrificing accuracy. This work demonstrates the feasibility and advantages of applying explainable machine learning techniques to the analysis of quantum dot measurements, paving the way for further advances in automated and transparent QD device tuning.

翻译：量子计算中量子点（QD）器件的快速发展，要求更高效、自动化的器件表征与调控方法。在调控过程中获得的许多测量结果以图像形式呈现，需要对这些图像进行恰当分析以指导后续调控步骤。此类图像中呈现的特征旨在捕捉被测量子点器件的特定行为或状态。若仔细考量，这些特征有助于量子点器件的控制与校准。此类图像的一个重要示例是所谓的\textit{三角图}，其以可视化方式呈现电流流动，并揭示对量子点器件校准至关重要的特性。虽然基于图像的分类工具（如卷积神经网络（CNN））可用于验证给定测量是否为\textit{良好}，从而决定是否启动下一阶段调控，但它们无法为\textit{不良}图像情况下应如何调整器件提供任何见解。这是因为CNN以牺牲预测和模型可理解性为代价来换取高精度。为改善这一权衡，最近的一项研究引入了一种基于Gabor小波变换的图像矢量化方法[1]。本文提出一种替代的矢量化方法，该方法通过对合成三角形进行数学建模来模拟实验数据。利用可解释提升机，我们证明这种新方法在不牺牲准确性的前提下，提供了更优的模型预测可解释性。本工作展示了将可解释机器学习技术应用于量子点测量分析的可行性与优势，为自动化、透明化的量子点器件调控的进一步发展铺平了道路。