Tensor-network frontends compress inputs for MPC-secured federated learning, after which a quantum-enhanced processor refines the aggregated latent features, with TTN+QEP showing the most balanced performance on PneumoniaMNIST.
Exponential concentration in quantum kernel methods
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ZZ quantum kernel with binary encoding reaches 66.3% accuracy on 11-feature parity tasks where binary RBF gets 54.3% and other classical methods ~50%, showing a complexity threshold for quantum advantage.
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Quantum-Enhanced Processing with Tensor-Network Frontends for Privacy-Aware Federated Medical Diagnosis
Tensor-network frontends compress inputs for MPC-secured federated learning, after which a quantum-enhanced processor refines the aggregated latent features, with TTN+QEP showing the most balanced performance on PneumoniaMNIST.
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Quantum Kernels for Parity-Structured Classification: A Hybrid Pipeline
ZZ quantum kernel with binary encoding reaches 66.3% accuracy on 11-feature parity tasks where binary RBF gets 54.3% and other classical methods ~50%, showing a complexity threshold for quantum advantage.