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：在物理科学领域，对图像数据鲁棒特征表示的需求日益增长：广义上的二维数据获取已广泛应用于众多领域，包括本文所关注的量子信息科学。尽管传统图像特征在此类场景中仍被广泛采用，但其应用正迅速被基于神经网络的技术所取代——这类技术往往以牺牲可解释性为代价获取高精度。为改善这种权衡关系，我们提出一种基于合成数据的特征提取方法，能够生成可解释性特征。通过使用可解释增强机（EBMs），我们证明了该方法能在不损失准确性的前提下实现更优的可解释性。具体而言，在目前仍需人工干预的量子点调控应用场景中，该方法展现出显著的应用价值。