量子核方法在真实消费者分类中表现良好，为当前硬件条件下的应用提供可行路径。
Quantum Kernel Methods: Convergence Theory, Separation Bounds and Applications to Marketing Analytics
- 设计量子-经典混合流程，用量子特征提取+量子支持向量机处理数据
- 在模拟和有限硬件上实现0.8100 F1值，召回率高达0.8609，精度与经典模型相当
- 适合关注量子计算落地、尤其是营销分析中的早期实践者
本研究探讨了在当前容错能力有限的近中期量子（NISQ）设备上，应用量子核方法进行真实消费者分类任务的可行性。提出一种混合流程，结合量子特征提取模块（QFE）与量子核支持向量机（Q-SVM），在模拟环境及浅层量子硬件上与经典和量子基线方法进行对比。在固定超参数条件下，所提Q-SVM达到0.7790准确率、0.7647精确率、0.8609召回率、0.8100 F1值和0.83 ROC AUC，表现出更高敏感性，同时保持与经典SVM相当的精度。结果被视为NISQ时代工作流与硬件集成的初步指标，而非最终基准。方法论上，设计契合近期关于量子-经典分离的理论框架，并通过类似XEB的方式验证资源开销，支持采用浅层但具表达力的量子嵌入，在硬件噪声限制下实现稳健可分性。
原文摘要 · Abstract (English)
This work studies the feasibility of applying quantum kernel methods to a real consumer classification task in the NISQ regime. We present a hybrid pipeline that combines a quantum-kernel Support Vector Machine (Q-SVM) with a quantum feature extraction module (QFE), and benchmark it against classical and quantum baselines in simulation and with limited shallow-depth hardware runs. With fixed hyperparameters, the proposed Q-SVM attains 0.7790 accuracy, 0.7647 precision, 0.8609 recall, 0.8100 F1, and 0.83 ROC AUC, exhibiting higher sensitivity while maintaining competitive precision relative to classical SVM. We interpret these results as an initial indicator and a concrete starting point for NISQ-era workflows and hardware integration, rather than a definitive benchmark. Methodologically, our design aligns with recent work that formalizes quantum-classical separations and verifies resources via XEB-style approaches, motivating shallow yet expressive quantum embeddings to achieve robust separability despite hardware noise constraints.
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