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In this study, a round-synchronous distributed quantum computing framework is developed for coherent controlled islanding under a fixed per-circuit qubit budget and attains optimal and operationally feasible partitions on every platform under noise.
A qubit-efficient hybrid quantum framework combining a physics-informed compact encoding with Lagrangian constraint handling and classical feasibility refinement is presented, offering a transferable approach for scaling constrained quantum optimization toward larger real-world applications on near-term hardware.
The proposed framework provides a feasible and scalable pathway for quantum optimization in large-scale power systems and substantially reduces quantum-resource demand and circuit complexity relative to monolithic QAOA, allowing large islanding problems to be addressed within current hardware limits.
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