English(EN) Benchmarking Quantum Kernel SVMs for Credit Card Fraud Detection: Performance Realities Under NISQ Constraints

量子SVM用于信用卡欺诈检测的基准测试

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-15 04:00

一篇新研究论文探讨了量子核支持向量机（QSVM）在信用卡欺诈检测中的有效性，这项任务因欺诈交易的稀少性而变得复杂。该研究将三种量子编码方法与经典模型进行了基准测试，并将合成少数类过采样（SMOTE）嵌入到量子工作流程中以解决类别不平衡问题。虽然经典模型在大数据集上达到了0.983的PR-AUC，但在约束其性能以匹配QSVM硬件限制时，其性能显著下降至0.750。最好的量子结果达到了0.730的PR-AUC，由于噪声和门错误，在物理量子硬件上的性能进一步下降到0.62。研究确定了Gram矩阵评估和特征压缩到有限量子比特寄存器上的扩展是近期量子核的关键瓶颈。 AI

影响强调了当前量子硬件在实际机器学习任务中的局限性，表明对于欺诈检测等复杂问题，经典方法仍然更优。

排序理由研究论文发布在arXiv上，详细介绍了实验结果。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.AI TIER_1 English(EN) · Hitanshi Arora, Mohit Swarnkar, Aswani Kumar Cherukuri · 2026-10-15 04:00

量子核SVM在信用卡欺诈检测中的基准测试：NISQ约束下的性能现实

arXiv:2610.17027v1 Announce Type: new Abstract: Detecting credit card fraud is notoriously difficult because fraudulent charges make up less than 0.2% of total transactions. Quantum Machine Learning (QML), particularly Quantum Kernel Support Vector Machines (QSVM), promises riche…

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量子核SVM在信用卡欺诈检测中的基准测试：NISQ约束下的性能现实

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