Quantum AI: Quantum Kernel Advantage

Classical diagnostic architectures operating on high-dimensional, frozen embeddings frequently encounter catastrophic spectral bottlenecking, particularly when tasked with the separation of imbalanced clinical manifolds, such as those derived from chest radiograph foundation models (MedSigLIP, RAD-DINO). This video dissects the empirical validation of "Quantum Kernel Advantage" as a rigorous mathematical mechanism for circumventing matrix rank collapse. By projecting classical inputs into an exponentially scaled, complex-valued Reproducing Kernel Hilbert Space (RKHS) via the Hilbert-Schmidt inner product, we demonstrate how quantum feature maps preserve representation sparsity and prevent the C-invariant optimization failures observed in standard linear SVMs. We move beyond the heuristic "quantum neural network" misnomer to establish a globally convergent, convex-optimized framework for medical diagnostics, where the non-linear scaling of the quantum kernel’s effective rank enables granular pattern extraction in feature spaces that remain computationally intractable for classical linear algebra. All rights w/ authors: Quantum Kernel Advantage over Classical Collapse in Medical Foundation Model Embeddings Sebastian Cajas Ord´o˜nez ,1, ∗ Felipe Ocampo Osorio ,1, 2 Dax Enshan Koh ,3, 4, 5 Rafi Al Attrach ,1 Aldo Marzullo ,6 Ariel Guerra-Adames ,7, 8 J. Alejandro Andrade ,9 Siong Thye Goh ,4, 10 Chi-Yu Chen ,11 Rahul Gorijavolu ,1, 12, 13, 14 Xue Yang ,15, 3, 16 Noah Dane Hebdon ,3 and Leo Anthony Celi 1, 17, 18 1MIT Critical Data, Massachusetts Institute of Technology, Cambridge, MA, USA 2Clinical Research Center, Artificial Intelligence Unit, Fundaci´on Valle del Lili, Cali, Valle del Cauca, Colombia 3Quantum Innovation Centre (Q.InC), Agency for Science, Technology and Research (A*STAR), 2 Fusionopolis Way, Innovis #08-03, Singapore 138634, Republic of Singapore 4Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (A*STAR