AI 中文总结
研究利用量子机器学习检测以太坊网络中的网络钓鱼，提出混合量子 - 经典集成框架，对比QSVM、VQC与统计方法、深度学习技术的效果，用QRAC编码数据提升模型性能，发现部分QML算法在量子处理器上有良好表现。
AI 中文摘要
本文探讨量子机器学习（QML）的潜力，具体评估量子支持向量机（QSVM）和变分量子分类器（VQC）在检测现实世界金融交易数据异常方面的表现。这些QML方法虽优于统计方法，但不及前沿深度学习技术。为此提出混合量子 - 经典集成框架，通过组合互补算法展示其在以太坊交易网络中检测网络钓鱼的有效性。QSVM单独或集成使用时误报率最低、召回率高。为增强单个模型，用新型级联量子随机存取编码（QRAC）方案编码数据并与流行的ZZ特征图比较，在模拟器和IBM Heron量子处理器上，QRAC对QSVM和VQC均有性能提升。某些QML算法在IBM Heron量子处理器上表现出显著弹性，接近模拟器性能。
英文摘要
This article explores the potential of Quantum Machine Learning (QML), specifically assessing a Quantum Support Vector Machine (QSVM) and a Variational Quantum Classifier (VQC) for detecting anomalies in real-world financial transaction data. While these QML methods outperform statistical methods, they fall short of cutting-edge deep learning techniques. To bridge this gap, we propose a hybrid quantum-classical ensemble framework that leverages the strengths of both domains. We demonstrate its effectiveness in detecting phishing in Ethereum transaction networks by combining complementary algorithms. The QSVM, whether used individually or in an ensemble, consistently delivered the lowest false negatives and higher recall rates, that are crucial for anomaly detection. To enhance individual models, we encoded the data using novel cascaded Quantum Random Access Coding (QRAC) schemes and compared it with the popular encoding ZZ feature map on both simulators and the IBM Heron quantum processor. For both QSVM and VQC, we consistently observed improvements (13% for QRAC-VQC and 3% for QRAC-QSVM) of QRAC over the ZZ feature map. Notably, certain QML algorithms exhibit remarkable resilience on the IBM Heron quantum processor, approaching simulator-level performance on devices with high quantum volume. This observation underscores the promise of QML despite hardware limitations.
