Optimizing Quantum Neural Network Architectures for Enhanced Computational Efficiency in Edge Computing Applications

Abstract

Quantum neural networks (QNNs) represent a pioneering convergence of quantum computing and artificial intelligence. This paper addresses the pressing challenge of optimizing QNN architectures specifically tailored for edge computing environments, where resource constraints and latency are critical. Utilizing a novel hybrid optimization algorithm that combines quantum annealing with classical gradient descent, we demonstrate significant enhancements in computational efficiency and accuracy in real-time data processing tasks. Our experimental results reveal that the proposed architecture outperforms conventional neural networks in both speed and energy consumption metrics. These findings underline the potential of QNNs in transformative edge computing applications, offering a pathway toward smarter, more efficient technological ecosystems.