Hybrid Classical–Quantum Transfer Learning Using Variational Quantum Layers for Low-Data Regimes

Supplementary Data Description This supplementary dataset provides the complete experimental results underlying Figures 4–9 of the manuscript. Each CSV file corresponds to one figure and reports mean performance values (and standard deviations where applicable), computed over multiple independent runs with different random seeds. Fig4_Low_Data_Performance.csv Contains classification accuracy (mean ± standard deviation) for Linear, MLP, and VQL heads across varying labeled target data fractions (1%, 5%, 10%, 20%). This dataset supports the low-data performance analysis presented in Fig. 4. Fig5_Parameter_Matched.csv Reports accuracy statistics for parameter-matched adaptation heads, including Linear Probe, MLP Adapter, Bottleneck Adapter, and the proposed VQL Head. This file enables fair comparison of adaptation strategies under comparable trainable parameter budgets (Fig. 5). Fig6_Domain_Shift.csv Provides source-domain accuracy, target-domain accuracy, and the resulting accuracy drop for each model under domain shift conditions. These results quantify robustness to distributional changes, as shown in Fig. 6. Fig7_Ablation.csv Contains ablation results evaluating the effect of quantum circuit depth and number of qubits on classification accuracy. This dataset supports the architectural analysis in Fig. 7. Fig8_Noise_Aware.csv Includes classification accuracy values under increasing quantum noise probabilities for all adaptation heads, enabling noise robustness evaluation corresponding to Fig. 8. Fig9_Convergence.csv Reports training accuracy as a function of training epochs for Linear, MLP, and VQL heads, illustrating convergence behavior and optimization stability (Fig. 9). Reproducibility Note All values are reported as mean performance over multiple independent runs, ensuring statistical reliability and reproducibility of the experimental findings.