Quantum Power-Grid Analytics
Authored a literature-grounded white paper and decision framework for power-grid quantum adoption, mapping nine algorithm families to grid workloads and screening them against QRAM, noise, and hybrid-workflow constraints.
Power-grid analytics is not one problem: unit commitment, load forecasting, fault classification, and power-flow analysis impose different mathematical and operational constraints. I evaluated quantum methods from those workload structures outward, instead of starting from generic quantum-advantage claims.
I mapped nine algorithm families—including QSVM, QNN, VQELM, QAOA, VQLS, and QSVD—to four grid workload classes. I then screened each mapping against encoding burden, circuit depth, noise sensitivity, classical-baseline strength, and hybrid integration requirements.
The analysis explicitly penalized methods that assume the existence of efficient Quantum RAM (QRAM) for large sensor datasets, distinguishing plausible near-term hybrid candidates (like VQC-based classifiers) from methods whose value depends on fault-tolerant hardware and deep circuits (like HHL for power-flow).
The resulting white paper and decision framework provide a practical basis for deciding what merits deeper validation and what remains premature. The framework treats literature-reported complexity and performance claims as conditional evidence, not as project-produced benchmarks.
This was a literature and feasibility assessment, not an implemented grid optimizer. Client-specific deliverables remain confidential, and this public portfolio omits any project-produced performance benchmarks or quantum-speedup claims.