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Quantum-Inspired Optimization Algorithms for Training Deep Neural Networks in Resource-Constrained Edge Computing Environments.

Narcisa Zlatan
Volume 5

Abstract

Resource-constrained edge computing environments require intelligent models that can operate under strict limitations related to memory capacity, computational power, energy consumption, communication bandwidth, and real-time response requirements. Although deep neural networks have achieved strong performance in many artificial intelligence applications, their training and deployment at the edge remain challenging due to high parameter complexity, slow convergence, large optimization search spaces, and sensitivity to hyperparameter configuration. This paper proposes a quantum-inspired optimization framework for training deep neural networks in resource-constrained edge computing environments. The proposed framework integrates quantum-inspired metaheuristic algorithms with lightweight neural architectures to enhance convergence efficiency, reduce computational overhead, and improve model performance under limited hardware resources. Quantum-inspired optimization mechanisms, including quantum-behaved Particle Swarm Optimization, quantum genetic search, quantum-inspired evolutionary operators, and probability amplitude-based solution representation, are employed to optimize critical neural network parameters and hyperparameters such as learning rate, weight initialization, pruning thresholds, dropout rates, batch size, number of hidden units, and layer configurations. The framework was evaluated using edge-oriented learning tasks, including image classification, anomaly detection, sensor-based activity recognition, and Internet of Things predictive monitoring, using datasets collected from embedded devices, smart sensors, wearable systems, and low-power edge nodes. A comprehensive preprocessing and compression pipeline was applied, including feature normalization, data augmentation, quantization-aware training, model pruning, knowledge distillation, and lightweight architecture selection to improve deployability on constrained platforms. Experimental results demonstrate that the proposed quantum-inspired optimization framework achieves superior performance compared with conventional training strategies, including stochastic gradient descent, Adam, RMSprop, classical genetic algorithms, Particle Swarm Optimization, and standard evolutionary optimization baselines. The optimized models achieve higher accuracy, faster convergence, reduced training loss, lower memory footprint, decreased inference latency, and improved energy efficiency across multiple edge computing scenarios. Furthermore, computational complexity analysis confirms that the proposed framework maintains strong scalability while reducing unnecessary parameter updates and resource-intensive search operations. The findings indicate that quantum-inspired optimization provides an effective, adaptive, and resource-aware solution for training deep neural networks at the edge, offering practical value for intelligent IoT systems, autonomous monitoring platforms, wearable healthcare devices, smart manufacturing applications, and real-time cyber-physical systems operating under limited computational resources.

 

Keywords

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