Quantum Feature Optimization for Enhanced Clustering of Blockchain Transaction Data
This work addresses clustering difficulties in blockchain data analysis, but it appears incremental as it builds on existing quantum and classical techniques without introducing a fundamentally new paradigm.
The study tackled the challenge of clustering high-dimensional and noisy blockchain transaction data by comparing classical, hybrid, and fully quantum clustering methods, finding that shallow quantum circuits significantly improved clustering performance.
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.