Distributed Quantum Computing for Fast Restoration of Power Distribution Systems With Grid-Forming IBRs

The combinatorial nature of restoration decisions, especially under increased grid-forming inverter-based resources (IBRs), renders classical optimization intractable for large systems. To potentially address this, this paper develops a coordination-free distributed quantum computing (DQC) framework for rapid and reliable distribution system restoration. A two-stage approach is developed, where microgrids (MGs) first restore loads locally using distributed quantum solvers, followed by a network restoration. To enable execution on noisy intermediate-scale quantum hardware, a compact quantum-compatible formulation is constructed via a tailored variable-reduction strategy and a constraint-scaling penalization method that yields minimal quadratic unconstrained binary optimization models with high fidelity. Case studies on modified IEEE 37- and 123-node feeders demonstrate that the proposed method accelerates restoration by up to 30% while maintaining high restoration optimality, and is compatible with both annealing- and circuit-based quantum platforms.