用量子神经网络优化区块链交易聚类特征，提升聚类效果
Quantum Feature Optimization for Enhanced Clustering of Blockchain Transaction Data
- 用随机初始化的量子神经网络生成量子随机特征，增强传统特征
- 浅层量子电路即可提取有效非线性表示，显著提升聚类性能
- 适合对区块链数据分析与量子机器学习感兴趣的读者
区块链交易数据具有高维度、噪声大和特征高度纠缠的特点，给传统聚类算法带来挑战。本研究对比三种聚类方法：(1) 对预处理特征应用经典K-Means聚类；(2) 混合聚类，将经典特征与通过随机初始化量子神经网络（QNN）提取的量子随机特征结合；(3) 全量子聚类，使用基于SwAV损失函数的自监督方式训练QNN，直接优化聚类特征空间。实验框架系统研究了量子电路深度和学习原型数量的影响，结果表明，即使使用浅层量子电路，也能有效提取有意义的非线性表示，显著改善聚类表现。
原文摘要 · Abstract (English)
Blockchain transaction data exhibits high dimensionality, noise, and intricate feature entanglement, presenting significant challenges for traditional clustering algorithms. In this study, we conduct a comparative analysis of three clustering approaches: (1) Classical K-Means Clustering, applied to pre-processed feature representations; (2) Hybrid Clustering, wherein classical features are enhanced with quantum random features extracted using randomly initialized quantum neural networks (QNNs); and (3) Fully Quantum Clustering, where a QNN is trained in a self-supervised manner leveraging a SwAV-based loss function to optimize the feature space for clustering directly. The proposed experimental framework systematically investigates the impact of quantum circuit depth and the number of learned prototypes, demonstrating that even shallow quantum circuits can effectively extract meaningful non-linear representations, significantly improving clustering performance.
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