对比量子编码与架构，提升金融欺诈检测准确率至94.3%
Comprehensive Analysis of VQC for Financial Fraud Detection: A Comparative Study of Quantum Encoding Techniques and Architectural Optimizations
- 采用三种量子编码和多种电路结构，优化变分量子分类器
- 圆环纠缠结构达93.3%准确率，优于线性与全连接模式
- 提出可视化方法，为实际量子机器学习部署提供指导
本文系统比较了用于金融欺诈检测的变分量子分类器（VQC）配置，涵盖三种量子编码技术及全面的架构变化。通过在多种纠缠模式、电路深度和优化策略下的实证评估，展示了量子优势，最高准确率达94.3%，使用ZZ编码方案。分析显示纠缠拓扑结构对性能影响显著，圆环纠缠始终优于线性（90.7%）和全连接（92.0%）模式，最优表现达93.3%准确率。研究引入新颖的量子电路可视化方法，并提供可操作的量子机器学习部署建议。系统性纠缠模式分析表明，圆环连接在表达能力与可训练性之间实现更优平衡，同时保持计算效率。研究成果为量子增强型欺诈检测系统提供了初步基准，并揭示了量子机器学习在金融安全应用中的潜在价值。
原文摘要 · Abstract (English)
This paper presents a systematic comparative analysis of Variational Quantum Classifier (VQC) configurations for financial fraud detection, encompassing three distinct quantum encoding techniques and comprehensive architectural variations. Through empirical evaluation across multiple entanglement patterns, circuit depths, and optimization strategies,quantum advantages in fraud classification accuracy are demonstrated, achieving up to 94.3 % accuracy with ZZ encoding schemes. The analysis reveals significant performance variations across entanglement topologies, with circular entanglement consistently outperforming linear (90.7) %) and full connectivity (92.0 %) patterns, achieving optimal performance at 93.3 % accuracy. The study introduces novel visualization methodologies for quantum circuit analysis and provides actionable deployment recommendations for practical quantum machine learning implementations. Notably, systematic entanglement pattern analysis shows that circular connectivity provides superior balance between expressivity and trainability while maintaining computational efficiency. These researches offer initial benchmarks for quantum enhanced fraud detection systems and propose potential benefits of quantum machine learning in financial security applications.
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