Quantum-Classical Hybrid Optimization for 500-Node Power Grid Stability: Efficiency Maximization via ARK5Q-200K Protocol.

Abstract This work introduces a hybrid quantum-classical optimization architecture designed to enhance the stability and efficiency of 500-node electrical power grids. By integrating Quantum Annealing (QA) with Graph Neural Networks (GNN) under the ARK5Q-200K protocol, we resolve the Quadratic Unconstrained Binary Optimization (QUBO) state of complex grid topologies. Our results demonstrate a reduction in transmission losses between 5-10% compared to classical GNN-based benchmarks, while maintaining a Hamiltonian precision residual of 1.48 mHa. We provide a rigorous differentiation between theoretical quantum sovereignty and the constraints of current NISQ hardware, establishing a high-fidelity baseline for the management of critical planetary and orbital energy infrastructures. This framework ensures global convergence in NP-hard energy manifolds, fulfilling the requirements for advanced infrastructure resilience.