We present a hybrid quantum–classical framework that couples a parameterized, hardware-imperfection-aware SQUID–Transmon surrogate simulation with deep learning to classify pancreatic radiotherapy CT/CBCT images from the Pancreatic-CT-CBCT-SEG collection. The classical backbone uses a pretrained EfficientNetB0 with bidirectional Long Short-Term Memory layers; the quantum component is a 4-qubit, shallow-depth circuit whose input angles are modulated by a dynamically computed error-mitigation factor derived from the gradient of an effective potential. We perform a grid search over three hardware-relevant parameters: a rescaling factor \(R\) (an amplitude/drive scaler in our surrogate) that modulates the effective potential and controls the input rotation amplitude, a secondary coupling strength \(\lambda \) , and the bare Josephson energy \(E_{J0}\) . In a 10-class subset experiment (top-dose patients), the hybrid model exhibits a performance maximum when \(R=0.95\) , reaching a test accuracy of up to 0.95 across several \((\lambda , E_{J0})\) settings. In this work, deviations of \(R\) , \(\lambda \) , and \(E_{J0}\) are treated as quasi-static hardware imperfections (systematic calibration offsets and slow drift) rather than as stochastic noise channels. We interpret this optimum as a balance between expressivity and over-rotation/leakage in our simulator when the effective drive is slightly reduced. To benchmark consistency on the full dataset, we additionally report a window-level evaluation over all 40 patients with the classical backbone alone: a single 80/10/10 split yields \(\text {train}=0.959\) , \(\text {val}=0.960\) , \(\text {test}=0.909\) accuracy (windows); stratified threefold cross-validation achieves \(0.876 \pm 0.005\) mean validation accuracy. Overall, our results indicate that such hardware-imperfection-aware hybrid models can be competitive with strong classical baselines while offering a physics-grounded knob for hardware-aware calibration; we discuss modeling assumptions and limitations (e.g., simplified hardware-imperfection model, connectivity, and task definition) to avoid overclaiming clinical readiness.
