用量子LSTM提升金融欺诈检测，速度更快、效果更优。
Toward Practical Quantum Machine Learning: A Novel Hybrid Quantum LSTM for Fraud Detection
- 将经典LSTM与量子电路结合，利用量子叠加和纠缠增强序列数据表征
- 每轮训练仅需45-65秒，远快于文献中动辄数分钟的同类模型
- 适合关注量子机器学习落地的金融科技与算法研究者
我们提出一种新型混合量子-经典神经网络架构用于欺诈检测，将经典长短期记忆（LSTM）网络与变分量子电路相结合。通过利用量子叠加与纠缠等现象，该模型增强了对序列交易数据的特征表示能力，捕捉纯经典模型难以建模的复杂非线性模式。采用全面的数据预处理流程对信用卡欺诈数据集进行清洗、编码、平衡与归一化，确保与基线模型公平比较。值得注意的是，该混合方法每轮训练时间仅为45-65秒，显著快于文献中同类架构通常需数分钟每轮的情况。通过统一反向传播过程联合优化经典与量子梯度，其中量子参数使用参数平移法则计算。实验评估显示，该模型在准确率、精确率、召回率和F1分数上均优于传统LSTM基线，验证了混合量子-经典技术在提升欺诈检测系统效率与性能方面的潜力。
原文摘要 · Abstract (English)
We present a novel hybrid quantum-classical neural network architecture for fraud detection that integrates a classical Long Short-Term Memory (LSTM) network with a variational quantum circuit. By leveraging quantum phenomena such as superposition and entanglement, our model enhances the feature representation of sequential transaction data, capturing complex non-linear patterns that are challenging for purely classical models. A comprehensive data preprocessing pipeline is employed to clean, encode, balance, and normalize a credit card fraud dataset, ensuring a fair comparison with baseline models. Notably, our hybrid approach achieves per-epoch training times in the range of 45-65 seconds, which is significantly faster than similar architectures reported in the literature, where training typically requires several minutes per epoch. Both classical and quantum gradients are jointly optimized via a unified backpropagation procedure employing the parameter-shift rule for the quantum parameters. Experimental evaluations demonstrate competitive improvements in accuracy, precision, recall, and F1 score relative to a conventional LSTM baseline. These results underscore the promise of hybrid quantum-classical techniques in advancing the efficiency and performance of fraud detection systems. Keywords: Hybrid Quantum-Classical Neural Networks, Quantum Computing, Fraud Detection, Hybrid Quantum LSTM, Variational Quantum Circuit, Parameter-Shift Rule, Financial Risk Analysis
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