Enhancing Interpretability of Quantum-Assisted Blockchain Clustering via AI Agent-Based Qualitative Analysis

TL;DR

This paper introduces a hybrid framework combining classical clustering metrics and AI Agent-based qualitative analysis to improve interpretability of quantum-assisted blockchain clustering, aiding sensitive domain applications.

Contribution

It presents a novel two-stage approach that enhances interpretability of quantum clustering models using AI agents for semantic explanation, bridging a gap in quantum blockchain analytics.

Findings

Quantum Neural Networks outperform random Quantum Features in quantitative metrics.

AI Agent uncovers nuanced differences and singleton cluster phenomena.

The approach confirms three-cluster configurations as optimal.

Abstract

Blockchain transaction data is inherently high dimensional, noisy, and entangled, posing substantial challenges for traditional clustering algorithms. While quantum enhanced clustering models have demonstrated promising performance gains, their interpretability remains limited, restricting their application in sensitive domains such as financial fraud detection and blockchain governance. To address this gap, we propose a two stage analysis framework that synergistically combines quantitative clustering evaluation with AI Agent assisted qualitative interpretation. In the first stage, we employ classical clustering methods and evaluation metrics including the Silhouette Score, Davies Bouldin Index, and Calinski Harabasz Index to determine the optimal cluster count and baseline partition quality. In the second stage, we integrate an AI Agent to generate human readable, semantic explanations of clustering results, identifying intra cluster characteristics and inter cluster relationships. Our experiments reveal that while fully trained Quantum Neural Networks (QNN) outperform random Quantum Features (QF) in quantitative metrics, the AI Agent further uncovers nuanced differences between these methods, notably exposing the singleton cluster phenomenon in QNN driven models. The consolidated insights from both stages consistently endorse the three cluster configuration, demonstrating the practical value of our hybrid approach. This work advances the interpretability frontier in quantum assisted blockchain analytics and lays the groundwork for future autonomous AI orchestrated clustering frameworks.