Automation of Quantum Dot Measurement Analysis via Explainable Machine Learning
<|reference_start|>Automation of Quantum Dot Measurement Analysis via Explainable Machine Learning: 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.<|reference_end|>
@article{schug2024automation,
    title={Automation of Quantum Dot Measurement Analysis via Explainable Machine
  Learning},
    author={Daniel Schug, Tyler J. Kovach, M. A. Wolfe, Jared Benson, Sanghyeok
  Park, J. P. Dodson, J. Corrigan, M. A. Eriksson, Justyna P. Zwolak},
    journal={arXiv preprint arXiv:2402.13699},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.13699},
    primaryClass={cs.CV cond-mat.mes-hall cs.LG}
}