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INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT

Development of AI-Driven Hybrid Framework for post Quantum security system using quantum Resilient cryptography

Gulshan Verma

Published May 1, 2026• 0 citations
Publisher
DOI
10.55041/ijsrem62608

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The rapid advancement of quantum computing threatens the foundations of classical cryptography, creating severe risks for resource-constrained IoT systems widely used in healthcare, autonomous vehicles, and industrial automation. Conventional algorithms like RSA and ECC are vulnerable to Shor's and Grover's algorithms, exposing IoT to passive and active quantum-based attacks. This manuscript proposes a quantum-safe framework integrating Post-Quantum Cryptography (PQC), Quantum Key Distribution (QKD), and Artificial Intelligence (AI) to ensure confidentiality, integrity, and resilience. The framework leverages lattice, hash, and code-based PQC, lightweight cryptographic protocols, hybrid security models, and efficient key management to protect IoT communications. By combining PQC, QKD, and AI-driven adaptive security, the framework offers scalable, sustainable, and future-proof protection in the post-quantum era.
The rapid convergence of quantum computing and artificial intelligence is reshaping computational paradigms across cryptography, optimization, sensing, and intelligent systems. This paper surveys and synthesizes recent developments at the intersection of quantum computing and AI, with particular attention to superconducting quantum hardware, quantum reinforcement learning, quantum key distribution optimization, and hybrid AI-driven post-quantum security frameworks. Drawing upon 2026 literature — including advances in high-speed predistortion architectures for flux-bias lines [1], enhanced quantum key distribution parameter optimization [2], quantum reinforcement learning for spectrum allocation in 6G networks [3], hybrid AI–post-quantum encryption for Internet of Medical Things (IoMT) systems [4], and measurement-consistent low-field MRI enhancement via k-space noise modeling [5] — we present a unified perspective on how quantum resources augment classical AI and how AI, in turn, accelerates the practical deployment of quantum technologies. We introduce a conceptual Quantum–AI Convergence Framework that maps hardware constraints, algorithmic opportunities, and security requirements. The analysis highlights persistent challenges in noise resilience, scalability, hybrid orchestration, and regulatory readiness, while outlining promising research directions for trustworthy quantum-enhanced intelligence.