PACE-QAOA: Physics-Constrained Quantum Optimization for Qubit-Efficient Power System Islanding
This work addresses the qubit bottleneck in applying quantum optimization to a specific NP-hard power system problem, offering a transferable approach for scaling constrained quantum optimization on near-term hardware.
The paper introduces PACE-QAOA, a qubit-efficient hybrid quantum optimization method for power system islanding that reduces phase-separator and per-layer gate complexity from quadratic to linear scaling for fixed island counts on sparse graphs. Evaluations on eight IEEE systems (9 to 89 buses) produce feasible, high-quality solutions under practical circuit and sampling budgets, with stable performance under device noise.
Controlled islanding partitions a stressed power network to limit disrupted power transfer while preserving operational integrity in every island. This NP-hard partitioning problem becomes increasingly demanding as networks grow, motivating quantum optimization as a complementary approach. However, limited qubit capacity restricts the scale at which conventional QAOA can address islanding. This paper develops a qubit-efficient hybrid quantum formulation that overcomes this barrier. A physics-informed compact encoding captures essential islanding decisions while exploiting grid structure, with formal guarantees preserving the feasible solution space and optimization objective. A qubit-efficient Lagrangian strategy combines quantum optimization with classical refinement to enforce operational constraints. Complexity analysis shows that for fixed island counts on sparse graphs, the formulation reduces phase-separator and per-layer gate complexity from quadratic to linear scaling. Evaluations on eight IEEE systems (9 to 89 buses) across multiple quantum backends produce feasible, high-quality solutions under practical circuit and sampling budgets. Factorial ablation attributes resource and runtime gains to the complementary effects of compact encoding and qubit-efficient Lagrangian constraint handling. Noise analysis demonstrates stable solution quality under device noise, and landscape diagnostics reveal smoother, more consistently scaled QAOA cost surfaces. These results provide a transferable pathway for scaling constrained quantum optimization toward larger real-world applications on near-term hardware.