Big Data Governance Through Quantum Error Correction and Automated Blockchain Frameworks

Abstract

The fast growth of the capture in blockchain networks raises challenges related to secure and efficient data governance, most particularly in a decentralized setting. Recent studies have been finding it difficult in solving the problems of data credibility, anomaly detection and error correction, under the quantum computing-circumstance with the compatible implementation of blockchain. A large number of existing models are not capable enough to handle the complexity and big data along with maintaining privacy and security. To address these monumental challenges in big data governance, this paper propositions a new approach by compiling a number of developed assistances, VGG16, Federated Learning, and IsoForest to identify anomalies and Variational Quantum Circuits (VQC) to correct errors in quantum computing. Our model has a 100 per cent recital in blockchain based data governance tasks, VGG16 is most efficient at 99.50% accuracy, 95.38% F1-Score, 98.52% AUC and 96.88 percent recall than other previous models in the same training. The proposed algorithm possesses a powerful classification capability of high dimensional data scattered similar to blockchain systems. We provide a scalable, privacy- preserving, and precise method of handling big data in addition to deeply integrating federated learning and quantum error correction (QEC) on a blockchain mind and environment. Our findings provide a strong basis for further research on quantum blockchain systems and can be applied to implementation issues such as error correction, privacy protection, and secure computing. The development of quantum error correction protocols, the optimization of models for larger datasets, and the investigation of possible integrations between quantum blockchain models to enhance security and performance are all examples of new research directions.