Quantum-Enhanced Hybrid Blockchain Framework for Adaptive Secure Storage and Authentication in IoT Smart Computing

The rapid advancement of Internet of Things (IoT)based smart computing systems demands cutting-edge security frameworks that ensure robust data protection, scalability, and adaptive authentication. Conventional blockchain-enabled security models often encounter challenges such as excessive computational overhead, inefficiencies in real-time adaptability, and vulnerability to emerging cyber threats. To overcome these issues, this paper introduces a groundbreaking Quantum-Enhanced Hybrid Blockchain Secure Model (QH-BSM), integrating a fusion of Quantum-Inspired Particle Swarm Optimization (QIPSO) and Genetically Modified Bat-Salp Swarm Optimization (GM-BSSO) for intelligent node selection, authentication, and decentralized data management. The proposed framework leverages Quantum Cryptography-Augmented Adaptive Proof-of-Work (QC-APoW) for dynamic block validation, enhancing security while minimizing processing delays. Additionally, an Adaptive Multi-Layer Hash Segmentation (AMHS) technique is introduced to optimize data distribution and storage across blockchain networks, ensuring seamless scalability and resource efficiency. Experimental evaluations demonstrate the superiority of QH-BSM in mitigating emerging threats such as quantum-enabled cyberattacks and distributed denial-of-service (DDoS) attacks, achieving reduced authentication latency (AL) and enhanced security robustness. This research establishes a pioneering pathway for secure, efficient, and quantum-resistant IoT smart computing systems.