Artificial intelligence and high-performance computing have helped electrical utilities modernize how they model, forecast, and manage grid operations. These tools already play a central role in maintaining reliability, balancing supply and demand, and planning new infrastructure. But as electrical grids become more distributed, data-intensive, and renewable-heavy, some of the most complex decision-making problems are becoming harder to manage with existing approaches alone. Utilities are increasingly asked to coordinate variable renewable generation, battery storage, electric vehicle (EV) charging, distributed energy resources, extreme weather scenarios, and shifting demand patterns across vast networks. Classical computing will remain essential to grid operations. In many cases, classical simulations, approximations, and optimization methods are highly effective, even for very large networks. The case for quantum computing is not that classical methods are collapsing. Rather, quantum computing may eventually enhance classical workflows in selected high-value areas where optimization complexity, scenario volume, and constraint density make better accuracy or faster exploration especially valuable. That is why power systems are emerging as one of the most promising application areas for hybrid quantum-classical computing. Where Quantum May Help Most The strongest near-term case for quantum in electrical grids lies in optimization. Many utility challenges require selecting the best, or a sufficiently good, solution from a large number of possible configurations. These include generation dispatch, unit commitment, optimal power flow, network reconfiguration, storage scheduling, EV charging coordination, infrastructure siting, and contingency-rich planning. These problems often involve many interdependent variables: generators, transmission lines, storage assets, loads, voltage limits, weather conditions, reliability constraints, and market rules. Depending on the formulation, some of these optimization problems can become computationally difficult as the number of assets, scenarios, and constraints grows. Today, utilities often manage this complexity with approximations, decomposed models, heuristics, and simplified assumptions. These methods are indispensable and frequently perform well. But they can