Hybrid Quantum-Classical Optimization for 118-Bus Power Systems

Abstract

This paper demonstrates a hybrid quantum-classical optimization approach for solving the optimal power flow (OPF) problem on the IEEE 118-bus test system. Classical OPF solvers scale poorly with network size and the combinatorial complexity introduced by discrete control variables such as transformer tap positions and switched shunt capacitors. We formulate a decomposed optimization where a quantum approximate optimization algorithm (QAOA) handles the discrete subproblem while a classical interior-point method solves the continuous relaxation. Using IBM's Qiskit runtime on a 127-qubit Eagle processor, we benchmark solution quality and computation time against purely classical solvers (IPOPT, Gurobi) and find that the hybrid approach achieves comparable solution quality with a 2.3x reduction in computation time for the discrete subproblem on systems with more than 50 discrete variables. We discuss the practical implications for real-time grid operations, the current limitations of quantum hardware noise, and a roadmap for scaling to transmission networks with thousands of buses. The paper includes reproducible code and benchmark datasets published through the Saral Systems Council data archive.

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