Quantum Annealing Based Power Grid Partitioning for Parallel Simulation

Parallel power-system simulation benefits from dividing a grid into balanced sub-networks while keeping the communication overhead between them small. This project formulates that graph-partitioning task as a quadratic unconstrained binary optimization problem and solves it using quantum annealing on a D-Wave QPU. The study evaluates solution quality, embedding constraints and time-to-solution on current hardware, and discusses how quantum optimization could eventually be integrated into CPU- and GPU-based parallel simulation workflows.