Benchmarking Evaluation of Quantum-Noise-Resilient Variational Classifiers for Computer Agriculture Disease Detection Under Near-Term Hardware Decoherence: An Artificial Intelligence Circuit-Fidelity Bound for Field Diagnostic Reliability

Variational quantum classifiers have been proposed as a route to plant-disease detection that could, in principle, exploit Hilbert-space features unavailable to classical convolutional networks, yet near-term superconducting hardware imposes gate error rates and decoherence times that bound the circuit fidelities any field-deployable diagnostic can attain. The analysis is theoretical and methodological: no observational data are used or claimed. A before-after-control-impact estimator is characterised under a declared variance structure with between-cluster SD = 6.0, residual SD = 4.5 and intraclass correlation = 0.640, across 2000 replications per design cell for cluster counts of 8, 16, 24 and 32 and effect sizes of 3, 5 and 8 percentage points. The design requires 54 clusters to detect a 5-percentage-point effect at 80% power. A fidelity-degradation theorem is derived showing that classifier accuracy decays exponentially in circuit depth once two-qubit gate error exceeds a threshold set by the ratio of decoherence time to gate time. The bound implies that field diagnostic reliability cannot be improved indefinitely by adding variational layers, and that benchmarking protocols for quantum agricultural classifiers must report circuit fidelity alongside accuracy.