Quantum Kernel Advantage over Classical Collapse in Medical Foundation Model Embeddings

• URL: http://arxiv.org/abs/2604.24597v1
• Date: Mon, 27 Apr 2026 15:21:53 GMT
• Title: Quantum Kernel Advantage over Classical Collapse in Medical Foundation Model Embeddings
• Abstract summary: We provide evidence of quantum advantage under noiseless simulation in binary insurance classification on MIMIC-C chest radiographs.<n>We propose two-tier fair comparison framework in which both classifiers receive identical features.<n> Eigen analysis reveals quantum kernel effective rank reaches 69.80 at q = 11, far exceeding linear kernel rank.
• Score: 5.903606299014097
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: We provide evidence of quantum kernel advantage under noiseless simulation in binary insurance classification on MIMIC-CXR chest radiographs using quantum support vector machines (QSVM) with frozen embeddings from three medical foundation models (MedSigLIP-448, RAD-DINO, ViT-patch32). We propose a two-tier fair comparison framework in which both classifiers receive identical PCA-q features. At Tier 1 (untuned QSVM vs. untuned linear SVM, C = 1 both sides), QSVM wins minority-class F1 in all 18 tested configurations (17 at p < 0.001, 1 at p < 0.01). The classical linear kernel collapses to majority-class prediction on 90-100% of seeds at every qubit count, while QSVM maintains non-trivial recall. At q = 11 (MedSigLIP-448 plateau center), QSVM achieves mean F1 = 0.343 vs. classical F1 = 0.050 (F1 gain = +0.293, p < 0.001) without hyperparameter tuning. Under Tier 2 (untuned QSVM vs. C-tuned RBF SVM), QSVM wins all seven tested configurations (mean gain +0.068, max +0.112). Eigenspectrum analysis reveals quantum kernel effective rank reaches 69.80 at q = 11, far exceeding linear kernel rank, while classical collapse remains C-invariant. A full qubit sweep reveals architecture-dependent concentration onset across models. Code: https://github.com/sebasmos/qml-medimage

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