Quantum Algorithm for Energy Efficient and Optimized IoT Clustering
Jaisimha Manipatruni · HAL (Le Centre pour la Communication Scientifique Directe) · 2025
The rapid expansion of the Internet of Things (IoT) has led to the deployment of large-scale, heterogeneous device networks with stringent energy efficiency requirements. Classical optimization algorithms often struggle with the combinatorial complexity of energy-efficient clustering in such networks. In this paper, we propose a quantum-classical hybrid approach using the Quantum Approximate Optimization Algorithm (QAOA) to address energy-efficient clustering in Industrial IoT. We provide a detailed mathematical formulation of the problem, map it to the MaxCut problem, and demonstrate how QAOA can be leveraged for this task. Through simulated QAOA cost landscapes, we illustrate the optimization process and discuss the potential of quantum algorithms for next-generation Industrial IoT energy management.