Quantum Chebyshev transform-based graph neural networks for financial fraud detection
Minrui XU, Bingyan Guan, Bethel Hui Ting LOKE +1 more
Financial fraud detection is a critical challenge requiring accurate identification of anomalous patterns in complex transaction networks. Graph Neural Networks (GNNs) have emerged as powerful tools for fraud detection by capturing relational structures among entities. Meanwhile, quantum computing offers new possibilities to enhance machine learning through high-dimensional Hilbert spaces and parallelism. In this paper, we propose a hybrid classical-quantum model called QCTGNN (Quantum Chebyshev Transform-based Graph Neural Network) for financial fraud detection. The QCTGNN integrates a classical graph neural network component based on Simplified Graph Convolutions (SGConv) with a quantum component that performs a Chebyshev polynomial-based transform via variational quantum circuits. The quantum component employs angle encoding of node features, Chebyshev polynomial embedding to enrich feature representations, multiple layers of parameterized RY rotations with entangling gates, and Pauli-Z measurements for readout. To address the noise and instability of Noisy Intermediate-Scale Quantum (NISQ) devices, we incorporate Noise-Aware Quantum Calibration (NQC) that adjusts for decoherence and readout bias in the quantum outputs. Our system also supports adaptive graph construction using correlation thresholds and temporal windows to capture dynamic transaction patterns, and uses SMOTE oversampling to rebalance class distributions in training data only. We present the architecture and algorithmic design of QCTGNN and provide an experimental evaluation on four financial transaction datasets. Across these benchmarks, QCTGNN matches or exceeds classical GNN baselines in ROC-AUC and PR-AUC, demonstrating the potential of quantum-enhanced graph learning for financial fraud analytics.
•Feb 1, 2026•0 citations