In the physical sciences, there is an increased need for robust feature representations of image data: image acquisition, in the generalized sense of two-dimensional data, is now widespread across a large number of fields, including quantum information science, which we consider here. While traditional image features are widely utilized in such cases, their use is rapidly being supplanted by Neural Network-based techniques that often sacrifice explainability in exchange for high accuracy. To ameliorate this trade-off, we propose a synthetic data-based technique that results in explainable features. We show, using Explainable Boosting Machines (EBMs), that this method offers superior explainability without sacrificing accuracy. Specifically, we show that there is a meaningful benefit to this technique in the context of quantum dot tuning, where human intervention is necessary at the current stage of development.

翻译：在物理科学中，对图像数据稳健特征表示的需求日益增加：广义上的二维数据采集现已广泛应用于包括量子信息科学在内的众多领域（本文即以此为例）。尽管传统图像特征在此类场景中仍被广泛使用，但其正迅速被基于神经网络的技术所取代——这类技术虽可提升准确率，却往往牺牲了可解释性。为改善这一权衡，我们提出一种基于合成数据的技术，能够生成可解释特征。通过可解释增强机（EBM），我们证明该方法在保证准确率的同时提供了更优的可解释性。具体而言，在目前仍需人工干预的量子点调谐场景中，该技术展现出显著优势。