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Proposes Parallel Quantum Feature Augmentation (PQFA), a hybrid quantum-classical framework that applies shallow variational quantum circuits to enhance fused multimodal features, outperforming classical baselines with fewer parameters.
Proposes a quantum-inspired hybrid classical-quantum framework for image classification using a mixture of experts, demonstrating improved performance and reduced failure rate on MNIST and Fashion-MNIST datasets.