mlsanityData to 14 Oct 2026

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

arXiv 2606.28834 ↗First posted 27 Jun 2026eess.SY cs.SYcited by 0

On the map: Deep reinforcement learning methods → Quantum reinforcement learning

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Quantum reinforcement learning, one of 15 directions in Deep reinforcement learning methods. Builds on 8 of its 26 references that are in the map. First posted on arXiv 27 Jun 2026.

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8 of 26 references.

References · 8

  1. 2020Variational Quantum Algorithmscited by 692
  2. 2019Control Barrier Functions: Theory and Applicationscited by 372
  3. 2020The effect of data encoding on the expressive power of variational quantum machine learning modelscited by 333
  4. 2019End-to-End Safe Reinforcement Learning through Barrier Functions for Safety-Critical Continuous Control Taskscited by 289
  5. 2019Variational Quantum Circuits for Deep Reinforcement Learningcited by 167
  6. 2018A predictive safety filter for learning-based control of constrained nonlinear dynamical systemscited by 118
  7. 2021Parametrized quantum policies for reinforcement learningcited by 90
  8. 2021Experimental Data-Driven Model Predictive Control of a Hospital HVAC System During Regular Usecited by 5

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