Quantum-Enhanced Cognitive AIoT for Real-Time Adaptive Decision-Making in Smart Infrastructure
Description
The proliferation of smart infrastructure demands intelligent systems capable of real-time, context-aware decision-making under dynamic conditions. This research proposes a novel hybrid framework—Quantum-Enhanced Cognitive AIoT (Q-CAIoT)—that integrates cognitive artificial intelligence of things (CAIoT) with quantum computing to significantly enhance adaptive decision-making processes. Leveraging quantum algorithms such as the Quantum Approximate Optimization Algorithm (QAOA) alongside federated learning of cognitive agents distributed at the edge, the system achieves superior performance in optimization speed, fault prediction accuracy, and energy efficiency.
Files
Steps to reproduce
The integration of cognitive capabilities into the Artificial Intelligence of Things (AIoT) has given rise to Cognitive AIoT (CAIoT), enabling edge devices to learn, reason, and adapt autonomously. However, scaling CAIoT systems to meet the demands of real-time decision-making in complex smart infrastructures remains challenging due to computational limitations. This paper presents a novel Quantum-Enhanced Cognitive AIoT (Q-CAIoT) framework that leverages quantum computing to accelerate optimization and learning processes within distributed cognitive agents.
Institutions
- Independent University
- North South University