Blockchain-enabled IoT and edge framework for secure data sharing, supply chain finance, and services via optimized multi-branch quantum neural network

Financial supply chains are increasingly burdened by low operational efficiency, fragmented and complex transaction processes, high error rates in financial risk prediction, and rising management costs. To overcome these issues, this paper proposes a Financial-Internet of Things (IoT) Supply Chain Risk Management framework that integrates blockchain and a Multi-Channel Triple-Branch Hybrid Quantum Convolutional Neural Network with Wader Hunt Optimization (MTHQCNN-WHO). The framework begins with IoT-enabled data collection, where real-time financial activities are captured through IoT sensors, and complemented by large-scale Financial Statement Datasets (FSDS). Data are securely stored on a Consortium Blockchain-Based Public Integrity Verification (CBPIV) system, ensuring tamper-proof and transparent record management. First, pre-processing will be conducted at the edge level by using the AMMDS-SCN process, followed by feature extraction utilizing the Bayesian Variational Transformer (BVT). After that, classification is achieved using a Multi-channel Triple-Branch Hybrid Quantum Convolutional Neural Network (MTHQCNN), further optimized by the Wader Hunt Optimization (WHO) algorithm for parameter tuning. Experimental results show the proposed model achieves 99.98% accuracy, 99.8% precision, 99.4% recall, and only 3.2% False Positive Rate, far surpassing existing methods. On the blockchain side, the system reaches 350 TPS transaction speed, 95 ms latency, 98.4% consensus success rate, and 110 Mbps throughput, proving it to be both highly accurate and efficient for secure financial risk management.