用量子网络提升信贷风险模型的可解释性，效果媲美随机森林。
Feature Ranking in Credit-Risk with Qudit-Based Networks
- 单量子位元网络联合编码特征与参数，统一演化生成可解释系数。
- 在台湾真实信贷数据上，宏平均F1得分媲美随机森林，优于逻辑回归。
- 提出两种可解释性度量，验证模型特征排序的可靠性与鲁棒性。
在金融领域，预测模型需兼顾准确率与可解释性，尤其在信贷风险评估中，模型决策影响重大。本文提出一种基于单量子位元（qudit）的量子神经网络（QNN），将数据特征与可训练参数共同编码于由完整李代数生成的统一酉演化中，既探索整个希尔伯特空间，又通过学习系数大小实现可解释性。我们在台湾真实、非平衡的信贷风险数据集上进行基准测试。该模型在宏平均F1分数上持续优于逻辑回归（LR），并达到随机森林（Random Forest）的水平，同时保持学习参数与输入特征重要性之间的透明对应关系。为量化可解释性，我们引入两个互补指标：(i) 模型特征排序与逻辑回归排序间的编辑距离；(ii) 特征投毒测试，即替换部分特征为噪声。结果表明，所提量子模型在保持竞争力的同时，为可解释量子学习提供了可行路径。
原文摘要 · Abstract (English)
In finance, predictive models must balance accuracy and interpretability, particularly in credit risk assessment, where model decisions carry material consequences. We present a quantum neural network (QNN) based on a single qudit, in which both data features and trainable parameters are co-encoded within a unified unitary evolution generated by the full Lie algebra. This design explores the entire Hilbert space while enabling interpretability through the magnitudes of the learned coefficients. We benchmark our model on a real-world, imbalanced credit-risk dataset from Taiwan. The proposed QNN consistently outperforms LR and reaches the results of random forest models in macro-F1 score while preserving a transparent correspondence between learned parameters and input feature importance. To quantify the interpretability of the proposed model, we introduce two complementary metrics: (i) the edit distance between the model's feature ranking and that of LR, and (ii) a feature-poisoning test where selected features are replaced with noise. Results indicate that the proposed quantum model achieves competitive performance while offering a tractable path toward interpretable quantum learning.
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