Quantum AIoT Framework for Cognitive Business Optimization: A Hybrid Quantum-Classical Approach for Real-Time Decision-Making
Description
In the era of Industry 5.0, businesses require decision-making capabilities that are not only fast but also context-aware and adaptive to volatile operational environments. Traditional Artificial Intelligence of Things (AIoT) systems—despite their automation potential—face performance bottlenecks in solving large-scale optimization problems in real time. The Cognitive Internet of Things (CIoT) introduces semantic understanding and contextual reasoning, but classical computing infrastructures limit its scalability.
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This paper proposes a Quantum AIoT Framework, a hybrid quantum-classical architecture designed to enable cognitive, real-time business optimization across logistics, finance, and manufacturing. By combining Quantum Approximate Optimization Algorithm (QAOA), Variational Quantum Eigensolvers (VQE), and Quantum Machine Learning (QML) with classical AI models, the framework demonstrates significant improvements in decision latency, optimization quality, and energy efficiency.
Institutions
- North South University