Deep unfolding with client-learned hyperparameters is claimed to improve quantum federated learning accuracy from roughly 55% to 90%, but the supporting proof and baseline data are not established.
Analyzing convergence in quantum neural networks: Deviations from neural tangent kernels,
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New Insights on Unfolding and Fine-tuning Quantum Federated Learning
Deep unfolding with client-learned hyperparameters is claimed to improve quantum federated learning accuracy from roughly 55% to 90%, but the supporting proof and baseline data are not established.