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The results suggest that, for highly periodic scientific monitoring time series, models incorporating strong seasonal or low-dimensional functional priors can match or outperform substantially more complex sequence architectures.
The results support three practices for emerging AI systems: expose stage-level energy, use reuse-aware accounting boundaries, and co-design the feature generator or accelerator with the classical readout.
A novel physics-guided linear mapper for quantum error mitigation that uses seven distinct interpretable features derived from circuit complexity and device calibration data, which reveals that circuit depth and CNOT count dominate error prediction, consistent with decoherence mechanisms.
In these small, idealised, classically simulated matched-family tasks, the support-basis DQFIM provides a useful data-dependent pre-training diagnostic of effective capacity on the retained data support and contributes predictive information beyond raw parameter count and structural metadata.
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