Frequency-modulated continuous-wave radar sensing often relies on labeled measurements that are costly, restricted, or difficult to collect at scale. This work evaluates physics-informed digital twins as controlled testbeds for early-stage quantum-classical radar learning. Two synthetic radar benchmarks are considered: unmanned aerial vehicle classification from range-Doppler maps and human fall detection from Doppler-time spectrograms. For both tasks, inputs are standardized, reduced using principal component analysis, and classified using either a radial basis function support vector classifier or a quantum support vector classifier. All quantum-kernel results are obtained using noiseless classical simulation; no quantum hardware is used, and no quantum-advantage claim is made. Across five random seeds, the quantum support vector classifier improves the UAV benchmark from four principal components onward, reaching an accuracy of 0.941 +/- 0.012 at eight components, compared with 0.880 +/- 0.029 for the classical baseline. On the fall-detection benchmark, both classifiers perform similarly, with a small quantum-kernel improvement at higher feature dimensions. A Gaussian-noise robustness study shows limited performance degradation across the tested noise levels, while preserving the UAV quantum-kernel gain. These results support digital twins as useful, controlled environments for radar-QML benchmarking prior to measured-data validation and hardware execution.