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.
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.
This paper investigates the integration of quantum error correction (QEC) techniques with blockchain consensus mechanisms to address inherent vulnerabilities and inefficiencies within traditional blockchain systems. The core claim centers around leveraging QEC's robust error detection and correction capabilities to bolster blockchain security, enhance consensus efficiency, and improve overall scalability. The proposed mechanism utilizes QEC to protect blockchain data from malicious attacks and environmental noise, thereby strengthening the consensus process. Furthermore, the application of QEC can accelerate the consensus algorithm, reducing latency and improving throughput. This work presents a novel approach to blockchain design, moving beyond classical cryptographic methods to incorporate the power of quantum mechanics for a more resilient and performant distributed ledger technology. The paper outlines the theoretical framework, proposes a specific QEC-enhanced consensus protocol, and discusses potential challenges and future research directions. The ultimate goal is to create a blockchain system that is inherently secure against attacks and capable of handling high transaction volumes with minimal delay. ---
The rapid evolution of digital technologies has intensified the need for secure, transparent, and efficient digital ecosystems. Blockchain technology, known for its decentralized and tamper-resistant nature, has emerged as a promising solution for ensuring data integrity and trust. Simultaneously, Quantum Machine Learning (QML) is gaining attention for its potential to revolutionize computational capabilities by leveraging quantum computing principles. This paper explores the integration of blockchain and QML to design secure digital ecosystems capable of handling complex data processing and ensuring robust security. Blockchain provides a distributed ledger for secure data storage and transaction validation, while QML enhances analytical capabilities through advanced pattern recognition and optimization techniques. The proposed framework addresses key challenges such as data privacy, scalability, and computational efficiency. Additionally, the study examines the role of smart contracts in automating processes and ensuring compliance within decentralized environments. Security concerns, including quantum threats to classical cryptographic systems, are also discussed, along with potential solutions such as quantum-resistant cryptography. The findings indicate that the integration of blockchain and QML can significantly enhance the security, efficiency, and scalability of digital ecosystems, making them suitable for applications in finance, healthcare, and enterprise systems.
This paper explores the application of quantum error correction (QEC) codes to enhance the security and resilience of blockchain technology. Traditional blockchains are vulnerable to attacks that exploit vulnerabilities in their distributed ledger systems. The core challenge lies in the immutable nature of the blockchain, where a single compromised node can potentially disrupt the entire network. This research proposes leveraging the powerful error-correcting capabilities of QEC codes to safeguard blockchain data. Specifically, we examine the encoding and decoding processes using various QEC codes, focusing on their ability to detect and correct errors introduced by malicious actors. The integration of QEC codes into the blockchain architecture can significantly improve its tolerance to attacks, ensuring data integrity and maintaining the trust inherent in the blockchain system. We present a framework for implementing QEC within blockchain transactions and discuss the potential performance implications. The primary goal is to demonstrate that QEC codes offer a viable path towards a more robust and secure blockchain ecosystem.
Financial fraud detection is challenged by decentralized data, severe class imbalance, and privacy constraints. This paper presents QuantumChain, a secure Quantum Federated Learning (QFL) framework that combines hybrid quantum-classical neural networks, encrypted federated aggregation, blockchain-based auditability, and quantum-secure communication. Each client trains a local hybrid model in which a variational quantum circuit is embedded between classical neural layers, while model updates are protected through homomorphic encryption, threshold secret sharing, and QKD-based keying. A permissioned blockchain records aggregation events and supports reputation-weighted trust among participants. We evaluate QuantumChain on financial transaction data using a compact, size-matched classical baseline to isolate the effect of the quantum layer. Results show that the HQNN achieves comparable accuracy while improving fraud-class recall in most settings, reaching 94.6% recall compared with 93.2% for the classical model. The Deep QLayer improves performance in full-data settings, suggesting that added circuit depth helps recover representational capacity when the shallow circuit becomes limited. Mixed-state simulations further show that the recall trend persists under non-ideal quantum evolution. In federated deployment with 10 heterogeneous clients, global accuracy increases from 97.7% to 98.8% over five rounds before stabilizing. These results show that QuantumChain can integrate depth-aware hybrid quantum models into a secure federated fraud-detection pipeline while maintaining stable global convergence.
The objective of this research is to develop a novel solution that employs both Quantum Machine Learning (QML) and blockchain for data fusion and intelligence in distributed environments. These include concerns about security, the ability to accommodate large datasets, and efficacy, which are fundamental when working with large amounts of data. In the initial stage, the study brings current approaches to integrative data analysis in distributed computing into focus and examines possible improvements by using quantum computing. The proposed framework integrates QML algorithms into the model to address the issue of data overload and enhance performance while using blockchain at the lower layer to address the problem of data hacking, loss, and manipulation. This framework is then applied and used to assess its effectiveness, efficiency, and security during various loads. The study provides novelty in the knowledge base since it has incorporated QML and blockchain to improve distributed data processing. It is proven that the proposed framework yields better clarified and safer data analysis of distributed systems when compared to the current methods. This has been proven by the outcome of the QML-enhanced framework which is improved in terms of its security, scalability, and efficiency from the previous framework. From such findings, it is possible that this could be the starting point in building subsequent computational models that are helpful in industries requiring complex and real-time data analysis. The significance of this study is that it discusses the future possibilities and utility of implementing cutting-edge technologies for effective and safe data storage.
The rapid expansion of IoT ecosystems has increased concerns regarding data privacy, security, and model integrity, particularly in environments vulnerable to sophisticated adversarial and quantum-enabled attacks. Traditional cryptographic and Federated Learning (FL) methods cannot keep up with the demands for confidentiality and robustness in vast, diverse networks. Therefore, this study presents a Quantum-Inspired Cryptographic Framework that uses Quantum Neural Networks, Post-Quantum Cryptography, and secure Federated Learning to enhance intrusion detection systems and provide quantum resilient communication. The methodology uses the UNSW-NB15 dataset, which has been cleaned, encoded, and normalized with reduced features balanced by SMOTE before splitting into 70 percent training and 30 percent testing. A QNN-based FL model can then be trained while Kyber-512 and NTRUEncrypt secure all updates to the model, as well as node communications, against both classical and quantum threats. The experimental results show that the proposed model significantly outperforms classical FL frameworks, achieving an accuracy of 98.1%, precision of 97.5%, recall of 97.9%, and F1-score of 97.7%, even under very challenging adversarial conditions. QICF is therefore a robust, privacy-preserving, and attack-resilient solution for next-generation large-scale IoT networks.