Quantum-Inspired Evolutionary Programming for Power System Optimization in the NISQ Era

Quantum-Inspired Algorithms, such as Quantum-Inspired Evolutionary Programming (QIEP), are strong candidates for solving combinatorial and optimization problems. While ongoing efforts aim to apply quantum algorithms to real-world challenges, their use cases remain limited. Therefore, in the current NISQ era, quantum-classical or quantum-hybrid approaches offer practical pathways for addressing complex problems.

We employ QIEP to solve several power system optimization tasks, including generation scheduling and the allocation of Flexible AC Transmission System (FACTS) devices, benchmarking its performance against conventional Genetic Algorithms. For the IEEE 9-Bus standard system, our simulation results show significant improvements: approximately 24% reduction in power loss for generation scheduling and nearly 35% cost reduction in FACTS device allocation. These findings suggest that QIEP, whether deployed as a quantum-inspired classical optimizer or integrated into hybrid quantum frameworks, can effectively address industry-relevant problems and pave the way toward achieving practical quantum advantage.