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This study systematically evaluates the Laplacian-based quantum semi-supervised learning (QSSL) approach across four benchmark datasets\u2014Iris, Wine, Breast Cancer Wisconsin, and Heart Disease. By experimenting with varying qubit counts and entangling layers, we demonstrate that increased quantum resources do not necessarily lead to improved performance. Our findings reveal that the effectiveness of the method is highly sensitive to dataset characteristics, as well as the number of entangling layers. Optimal configurations, generally featuring moderate entanglement, strike a balance between model complexity and generalization. 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