Quantum Computing Research
Post-classical computation for grid optimization. Quantum computing is not speculative for power systems - it is the next computational paradigm for optimization problems that classical computers handle poorly.
Power system optimization is fundamentally a combinatorial challenge. Unit commitment, optimal power flow, and contingency analysis all involve search spaces that grow exponentially with system size. Classical solvers approximate. Quantum computation offers a different computational substrate - one where certain classes of these problems may yield to more efficient solution methods.
SARAL's quantum research is deliberately grounded in power systems reality. QuantumGridOS is our research platform, not a product. It exists to test whether quantum approaches yield genuine advantage on real grid optimization problems, under realistic noise models, with honest benchmarking against classical alternatives. This is applied research with intellectual honesty about what quantum can and cannot yet deliver.
Featured Publications
QuantumGridOS Research Platform
SARAL's research platform for exploring quantum algorithms applied to power system optimization. QuantumGridOS is not a product - it is an open research instrument for testing quantum approaches to unit commitment, optimal power flow, and grid resilience under uncertainty.
Quantum-in-the-Loop Architecture
Hybrid computational architecture that integrates quantum processors into classical simulation loops for power system analysis. Drawing on methodology lineage from NREL, this research explores how quantum co-processors can accelerate specific computational bottlenecks in grid modeling.
All Publications
Grid Resilience Under Quantum Computing Paradigms
Examining how quantum computing capabilities - both near-term NISQ devices and fault-tolerant future systems - reshape the landscape of grid resilience analysis, contingency planning, and scenario modeling.
Active Research Directions
Quantum computing for power systems is an emerging field. SARAL's research is focused on the following active directions, with results published as they mature.
NISQ Algorithm Benchmarking
Systematic benchmarking of near-term quantum algorithms against classical solvers on power system optimization problems of increasing scale.
Hybrid Quantum-Classical Workflows
Designing computational workflows where quantum processors handle specific sub-problems within larger classical simulation frameworks for grid analysis.
Quantum Error Mitigation for Grid Models
Developing error mitigation strategies specific to the structure of power system optimization problems on noisy quantum hardware.