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：广义上的二维数据采集已广泛应用于大量学科，包括本文关注的量子信息科学。尽管传统图像特征在此类场景中应用广泛，但其正迅速被基于神经网络的技术所取代——这些技术往往以牺牲可解释性为代价换取高精度。为改善这一权衡，我们提出一种基于合成数据的技术，可生成具有可解释性的特征。通过使用可解释性提升机（EBM），我们证明该方法能在不牺牲精度的前提下实现更优的可解释性。具体而言，我们证明该技术在量子点调谐场景中具有显著优势——在当前发展阶段，该场景仍需要人工干预。