CAREER: Q-GRID: Transforming Next-Generation Power Grid Computation with a Unified Quantum-Classical Paradigm

This NSF CAREER project aims to develop advanced computing tools that can help electric power grids operate more reliably as they become increasingly complex. The rapid growth of distributed energy resources, electric vehicles, data centers, advanced sensors, and power-electronic devices is creating unprecedented demands on grid monitoring, forecasting, decision-making, and emergency response. Existing computing methods may struggle to evaluate the large number of possible operating conditions and produce high-quality decisions within the limited time available to grid operators. The project will bring transformative change by combining quantum computing with classical computing methods in a unified framework designed specifically for power system applications. This will be achieved by leveraging distinctive quantum capabilities, including probabilistic processing, superposition, entanglement, and tunneling, to improve grid visibility, forecast changing system conditions, prevent major disruptions, and restore electric service following outages. The intellectual merit of the project includes advancing fundamental knowledge at the intersection of quantum computing and power engineering and determining when quantum-classical methods can deliver meaningful computational benefits. The broader impacts of the project include strengthening the reliability and resilience of the nation’s critical energy infrastructure and preparing students for emerging careers that connect energy systems, advanced computing, and quantum technologies. The project will establish a power-system-informed quantum-classical computing framework for grid monitoring, modeling, optimization, and control. First, it will develop hybrid quantum-classical algorithms to determine where sensors and communication resources should be placed and how available measurements should be selected efficiently during real-time operation, particularly under uncertain conditions. Second, it will develop quantum-enhanced data fusion and forecasting methods to improve multi-timescale situational awareness and spatiotemporal forecasting from partially observed sensor data, including both fast equipment-level changes and slower system-wide trends. Third, it will develop resilience-oriented decision methods for preventive control and rapid service restoration following disruptive events. The research will use realistic power system models and data while accounting for the practical limitations of current quantum hardware. Performance will be compared to classical and quantum-inspired methods using measures such as solution quality, computation time, scalability, forecasting accuracy, and robustness. A quantum-hardware-aware suitability evaluation will identify the conditions under which the unified quantum-classical paradigm provides clear benefits, thereby establishing a realistic foundation for future quantum applications in energy and other complex infrastructure systems. By advancing computational capabilities for grid monitoring, modeling, optimization, and control, this research will strengthen power system analysis and contribute to the strategic goals of the National Quantum Initiative. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.