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Quantum Neural Networks for Edge Computing by costanturco is a document available to read on EtoBox.

This study explores the use of quantum-inspired algorithms in neural networks for decentralized edge computing. A hybrid quantum-classical architecture is proposed, which improves data locality and reduces latency in federated learning, achieving a 23% improvement in convergence rates over classical models. The findings highlight the effectiveness of quantum amplitude amplification techniques in resource-constrained environments.

Author
costanturco
Language
EN