Optimizing Energy Management and Load Balancing Through AI-Driven Quantum Approximate Optimization

The integration of Quantum Approximate Optimization Algorithm (QAOA) and Quantum Annealing (QA) offers a promising approach to addressing energy management and load balancing challenges in modern power systems. This study explores the application of these quantum computing techniques, coupled with Artificial Intelligence (AI), to optimize energy distribution across smart grids. The proposed hybrid model leverages the problem-solving capabilities of QAOA for discrete optimization and the real-time adaptability of QA for minimizing energy consumption and operational costs. AI techniques are employed to predict energy demand, balance loads, and ensure efficient resource allocation. Simulations were conducted on a smart grid scenario involving renewable energy sources, varying demand, and distributed energy generation. The results demonstrate that the hybrid model achieved a 25% improvement in load balancing efficiency and a 30% reduction in energy wastage compared to traditional optimization methods. Additionally, the model reduced peak energy demand by 20%, contributing to overall grid stability. The quantum-enhanced approach also decreased computational complexity by 40%, enabling faster decision-making in dynamic environments. These findings highlight the potential of combining quantum algorithms with AI for sustainable and efficient energy management.