A new Security model for blockchain Using Quantum Computing

Azhar Ali Khan, Muhammad Umar, Imran Khurshid +3 more

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.

Kazuki Ikeda

Rehan Ahmad, Kemal Polat, Surendra Kumar Shukla +1 more

Data security is an essential attribute of all the domains of computing, including quantum computing (QC). To secure the data, blockchain (BC) blockchain (BC) technology has shown promising results. Quantum algorithms when embedded with BC for securing the data stored on data centers, cloud, and system has shown promising results. The prime advantage of quantum BC is that it makes tampering of BC impossible. Quantum BC will handle the security issues effectively, persist to quantum computers and current BC framework using quantum protocols and quantum resistant technologies. This chapter provides a comprehensive perspective on (i) integrating conventional BC frameworks with quantum computer; (ii) the long-term viability of BC technology with quantum computing to secure data; (iii) solutions offered by quantum BC to the vulnerabilities encountered by classical BC system to enhance efficiency, scalability, and security; (iv) discuss some distinct approaches for evaluation of the effectiveness of BC consensus mechanisms in the presence of quantum computing; and (v) quantum threats and how quantum-safe BC mitigates them are discussed. In addition to these, the future prospects of research in the domain of QC and BC are given.

Km Meenakshi, Rakhi Chauhan, Deepshikha Bhargava

Hamed Taherdoost, Yousef Farhaoui

Mina Younan, Mohamed Elhoseny, Abdelmgeid Amin Ali +1 more

Recently, Internet of Things (IoT) and Blockchain (BC) are coupled to present smart and secure applications. Quantum Computing (QC) is a new paradigm that imposes itself as a main technology in the near future, especially in the 6G era, due to its powerful computations. As a result, immigration of the IoT and BC to the QC has to be studied and prepared. This paper provides a brief review of relevant recent work to identify opportunities for optimal integration. Besides, a new paradigm called Quantum Chain of Things (QCoT) is proposed and its perspective architecture is presented and verified into two parts: (a) BC-IoT using Arduino, and (b) QC-IoT using IBM Qiskit simulator. Finally, presented knowledge such as current challenges, threads, and integration opportunities could help researchers to investigate new trends and future directions.