Quantum-Classical Hybrid Architectures for Blockchain and Contextual AI
Eman A. Sayed, Sara M. Mosaad · Multicriteria Algorithms with Applications · 2025
The convergence of quantum computing, blockchain technologies, and contextual artificial intelligence offers a transformative opportunity to meet the growing demands of secure, adaptive, and decentralized systems. However, most existing approaches focus on isolated or pairwise integrations of these technologies and do not provide a comprehensive architectural synthesis. This paper introduces a quantum-classical hybrid architecture that unifies quantum-enhanced cryptographic processing, blockchain-based trust mechanisms, and context-aware AI reasoning within a modular and interoperable system design. The proposed architecture consists of four interoperable layers: a quantum layer for cryptographic functions and computational enhancements, a classical layer for coordination and orchestration, a contextual AI layer for real-time inference and adaptation, and a blockchain layer for transparent and tamper-resistant record keeping. A key innovation of this architecture lies in its use of contextual AI models, such as transformers, to interpret dynamic environments and guide decisions across quantum and blockchain components. This paper presents use cases in healthcare, finance, and supply chain domains which are theoretical, yet based on realistic challenges and operational demands. These scenarios illustrate how each layer of the architecture contributes to practical decision-making, enhancing the feasibility of future deployments. The paper also discusses implementation challenges such as quantum hardware limitations, integration complexity, and data privacy, and outlines a trajectory for advancement through quantum middleware, explainable AI, and post-quantum cryptographic integration. By aligning emerging technologies within a unified structure, this study offers a forward-looking blueprint for secure, intelligent, and context-aware systems in decentralized environments.