Machine Learning Ensures Quantum-Safe Blockchain Availability

Abstract

ABSTRACT This study explores quantum computing and blockchain, focusing on quantum-safe algorithms. As quantum computing progresses, it threatens blockchain cryptography, necessitating quantum-resistant/safe algorithms. Using statistical analysis and simulations to address future cyber threats, we analyze quantum-safe cryptography in blockchains. We pioneer machine learning models to assess quantum-safe algorithm performance across encryption, key generation, signatures, speed, scalability, energy efficiency, and security. Our analysis, referencing Ethereum’s Ether values, pinpoints areas for quantum-safe cryptography improvements, addressing limitations and proposing solutions to strengthen security and efficiency. In experiments, we observed a 15% increase in encryption strength, 10% enhanced key generation efficiency, and improved reliability parameters. These findings stress the importance of machine learning in quantum-safe cryptography for widespread adoption and long-term security against quantum threats. In conclusion, our research paves the way for the integration of quantum-safe algorithms, ensuring the resilience of blockchain systems in the face of quantum advancements.