Quantum Kernels are projected to provide early-stage usefulness for quantum
machine learning. However, highly sophisticated classical models are hard to
surpass without losing interpretability, particularly when vast datasets can be
exploited. Nonetheless, classical models struggle once data is scarce and
skewed. Quantum feature spaces are projected to find better links between data
features and the target class to be predicted even in such challenging
scenarios and most importantly, enhanced generalization capabilities. In this
work, we propose a novel approach called Systemic Quantum Score (SQS) and
provide preliminary results indicating potential advantage over purely
classical models in a production grade use case for the Finance sector. SQS
shows in our specific study an increased capacity to extract patterns out of
fewer data points as well as improved performance over data-hungry algorithms
such as XGBoost, providing advantage in a competitive market as it is the
FinTech and Neobank regime.

量子内核被认为在量子机器学习的早期阶段能够提供有用性，但是高度复杂的经典模型很难在不丧失解释能力的情况下超越其，并且当数据稀缺且倾斜时经典模型遇到困难。在这项工作中，我们提出了一种称为系统量子分数（SQS）的新方法，并提供了初步结果，表明在金融部门的生产级用例中，它比纯粹的经典模型具有潜在优势。SQS 在我们的具体研究中表现出从较少数据点中提取模式的能力增加，以及在像 XGBoost 这样需要大量数据的算法上改善了性能，从而在竞争激烈的市场中提供了优势。