Q-DASC: State-of-the-Art Safe Quantum Control for HVAC under Local Model Misspecification

Variational quantum reinforcement learning offers a compact policy class for building-energy control, but it inherits a deployment weakness shared by learned controllers: when the thermal model is locally wrong, a policy that appears safe on the model can violate occupant comfort in the real building. Guarantees that depend on noisy quantum read-out are also insufficient for safety-critical control. We address this gap with Q-DASC, Discrepancy-Attributed Safe Quantum Control. Q-DASC wraps a variational-quantum-circuit (VQC) policy with a certified classical safety layer that discovers misspecified operating regimes with false-discovery-rate control, repairs their local thermal gains with shrinkage, projects the proposed quantum schedule onto the repaired comfort-feasible set, and attributes residual violations to policy error, model error, or physical limits. Because the final certificate is produced by classical projection, comfort feasibility is invariant to finite-shot and depolarizing read-out noise. On real BOPTEST building emulators across three buildings, two localized misspecifications, and three seeds, Q-DASC reduces average comfort violation from 26.0\% for the raw VQC controller and 55.3\% for a model-trusting scheduler to 0.02\%, matching a clairvoyant oracle, and remains at 0.24\% under NISQ read-out noise. A repair-aware VQC variant reaches 0.00\% violation and reduces projection intervention, while the default Q-DASC keeps lower energy and stronger observational-data behavior. The same wrapper transfers to EnergyPlus heating and cooling benchmarks and to real hospital air-handling-unit data. These results establish a safety-efficiency frontier for deploying quantum policies in physics-constrained control.