让量子神经网络可解释，提升信贷评分透明度
IQNN-CS: Interpretable Quantum Neural Network for Credit Scoring
- 融合变分量子网络与事后解释技术，实现结构化可解释性
- 在两个真实数据集上表现稳定，预测性能媲美传统模型
- 提出新指标ICAA，量化不同风险类别的特征贡献差异
信贷评分是金融领域高风险任务，模型决策直接影响个人信用获取，需满足严格监管要求。尽管量子机器学习（QML）提供新计算能力，但其黑箱特性阻碍了在注重透明度领域的应用。本文提出IQNN-CS，一种面向多类别信贷风险分类的可解释量子神经网络框架。该架构结合变分量子神经网络与专为结构化数据设计的事后解释技术。针对QML缺乏结构化可解释性的痛点，提出跨类别归因对齐（ICAA）新指标，量化不同预测类别间的归因差异，揭示模型如何区分各类信用风险。在两个真实世界信贷数据集上评估，IQNN-CS展现出稳定的训练动态、具有竞争力的预测性能以及更强的可解释性。结果表明，该方法为金融决策中的透明、可问责量子模型提供了可行路径。
原文摘要 · Abstract (English)
Credit scoring is a high-stakes task in financial services, where model decisions directly impact individuals' access to credit and are subject to strict regulatory scrutiny. While Quantum Machine Learning (QML) offers new computational capabilities, its black-box nature poses challenges for adoption in domains that demand transparency and trust. In this work, we present IQNN-CS, an interpretable quantum neural network framework designed for multiclass credit risk classification. The architecture combines a variational QNN with a suite of post-hoc explanation techniques tailored for structured data. To address the lack of structured interpretability in QML, we introduce Inter-Class Attribution Alignment (ICAA), a novel metric that quantifies attribution divergence across predicted classes, revealing how the model distinguishes between credit risk categories. Evaluated on two real-world credit datasets, IQNN-CS demonstrates stable training dynamics, competitive predictive performance, and enhanced interpretability. Our results highlight a practical path toward transparent and accountable QML models for financial decision-making.
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