The key challenges of performance prediction in entrepreneurship stem from the heterogeneous, multidimensional, nonlinear, and imbalanced characteristics of entrepreneurial behaviors, i.e., ambitiousness and risk preference. While machine learning algorithms, including Support Vector Machines, Random Forests, XGBoost, and LightGBM, have been purported and adopted to model entrepreneurial outcomes, their optimization saturation effect in performance prediction between 60% and 79% is observed, with little novelty in representational expansion by resampling and ensemble engineering approaches. To overcome the limits of existing models, this paper adopted a stability-guided quantum-enhanced hierarchical QAOA-QSVM framework whereby hierarchical Quantum Approximate Optimization Algorithm (QAOA) feature selection interacted with Quantum Kernel Support Vector Machine for Entrepreneurial Skill (QSVM). Features extracted from 219 university students' gender, race, personality, and university attributes were successively selected by formulating the QUBO problem of feature optimization and solving with QAOA. The stability consensus was weighed by stratified five-folds to improve reproducibility and robustness. Selected features were eventually embedded in an entangled quantum Hilbert space of the ZZFeatureMap to enable high-order nonlinear feature interactions with quantum kernel learners. The results indicated that our quantum approach surpassed a classical RBF-SVM on information from university students by nearly 22% (83.09% vs. 60.68%) and reached an 86.84% recall for entrepreneur prediction. We conclude that quantum representation learning structures can provide more insightful feature embedding ways, enabling novel transformers in talent pipeline building of entrepreneurship courses. This study also extends a meta-complementary theory between quantum machine learning and entrepreneurship.