基于可解释机器学习的量子点测量分析自动化
Automation of Quantum Dot Measurement Analysis via Explainable Machine Learning
- University of Maryland（马里兰大学）
- University of Wisconsin-Madison（威斯康星大学麦迪逊分校）
- National Institute of Standards and Technology（美国国家标准与技术研究院）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对量子点调谐中图像分类工具可解释性不足的问题，提出基于合成三角形数学建模的向量化方法，结合可解释提升机在保证精度的同时提升预测可解释性，助力量子点器件自动化调谐。
AI中文摘要：
面向量子计算的量子点（QD）器件发展迅速，亟需更高效的自动化器件表征与调谐方法。本研究验证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谐的进一步发展铺平了道路。调谐过程中采集的许多测量数据以图像形式存在，需对其进行恰当分析以指导后续调谐步骤。这类图像中的特征天然对应被测量子点器件的特定行为或状态，仔细分析这些特征可辅助量子点器件的控制与校准。此类图像的一个重要代表是所谓的triangle plots（三角图），它可直观呈现电流流动情况，揭示对量子点器件校准至关重要的特性。卷积神经网络（CNN）等基于图像的分类工具虽可用于验证给定测量结果是否“合格”，从而决定是否启动下一阶段调谐，但在图像“不合格”时，这类工具无法提供任何关于器件应如何调整的洞见——这是因为CNN为追求高精度牺牲了预测与模型的可解释性。为改善这一权衡，近期一项研究提出了一种依赖Gabor小波变换的图像向量化方法（Schug等人，2024，《XAI4Sci会议论文集：AAAI 2024科学领域可解释机器学习研讨会（加拿大温哥华）》，第1-6页）。本文提出了另一种向量化方法，该方法通过对合成三角形进行数学建模来模拟实验数据。借助可解释提升机，我们证明这种新方法在不牺牲精度的前提下，大幅提升了模型预测的可解释性。
英文摘要：
The rapid development of quantum dot (QD) devices for quantum computing has necessitated more efficient and automated methods for device characterization and tuning. 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. 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 (Schug $\textit{et al.}$ 2024 $\textit{Proc. XAI4Sci: Explainable Machine Learning for Sciences Workshop (AAAI 2024) (Vancouver, Canada)}$ pp 1-6). 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.
