发表机构
Singapore Management University（新加坡管理大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究基准测试了经典与光子量子求解器在金融风险检测QUBO特征选择中的性能，发现求解器性能依赖特征空间景观，光子方法在特定场景下可匹配或超越经典方法。
AI 中文摘要
针对信用卡欺诈和消费者违约检测等不平衡分类任务的特征选择，需要在预测相关性、特征间冗余和计算可行性之间取得平衡。我们在两个数据集上，对十三种特征选择方法，基准测试了三种计算范式：经典分支定界优化（Gurobi）、光子熵计算（QCI Dirac-3）和模拟光子玻色子采样（Piquasso）。这两个数据集分别是ULB信用卡欺诈数据集（30个特征）和AmEx消费者违约数据集（159个特征）。每种方法都被路由到与其数学结构匹配的求解器。在ULB数据集上，Dirac-3的MI-Spearman方法使用30个特征中的13个即可匹配全特征模型的性能（五次运行的平均F1为0.873±0.023，最佳运行结果为0.896），而Piquasso在k=5时是最佳方法。在AmEx数据集上，性能随特征预算稳步提升，所有范式仅在接近完整特征集时才达到F1≈0.80。Gurobi和Dirac-3在相同方法上的大多数差异都在运行间波动范围内；较大的差距出现在认证最优解泛化能力差的情况下，最显著的是AmEx数据集上k=25时的距离相关性（Gurobi的F1为0.422，而Dirac-3的平均值为0.746）。在匹配的预算下，ULB数据集上不同方法的F1变异大约是AmEx数据集的十倍，我们将此归因于预测信号在每个特征空间中的集中程度。
英文摘要
Feature selection for imbalanced classification tasks such as credit card fraud and consumer default detection requires balancing predictive relevance, inter-feature redundancy, and computational feasibility. We benchmark three computing paradigms, classical branch-and-bound optimization (Gurobi), photonic entropy computing (QCI Dirac-3), and simulated photonic boson sampling (Piquasso), across thirteen feature-selection methods on two datasets: ULB Credit Card Fraud (30 features) and AmEx consumer default (159 features). Each method is routed to the solver matched to its mathematical structure. On ULB, Dirac-3 MI-Spearman matches the all-features model using 13 of 30 features (mean F1 0.873 +/- 0.023 over five runs, best run 0.896), and Piquasso is the best method at k=5. On AmEx, performance rises steadily with the feature budget and every paradigm approaches F1 = 0.80 only near the full feature set. Most differences between Gurobi and Dirac-3 on identical methods fall within run-to-run variation; the large gaps occur where the certified optimum generalizes poorly, most sharply for distance correlation on AmEx at k=25 (Gurobi F1 = 0.422 vs. a Dirac-3 mean of 0.746). At matched budgets, F1 varies about ten times more across methods on ULB than on AmEx, which we trace to how concentrated the predictive signal is in each feature space.
Comments39 Pages, 41 Tables, 3 Figures
