Quantum-Inspired Neural Architecture Search (Q-NAS)

Abstract

Neural Architecture Search (NAS) has revolutionized deep learning by automating the design of optimal neural network architectures. However, classical NAS methods suffer from high computational costs and inefficient search processes. In this research, we introduce Quantum-Inspired Neural Architecture Search (Q-NAS), leveraging quantum computing paradigms such as quantum annealing and variational quantum circuits (VQC) to optimize NAS for edge-device deployment. We explore hybrid quantum-classical frameworks that improve search efficiency and reduce energy consumption. Our work provides comparative analyses, proposes novel quantum search strategies, and benchmarks Q-NAS against traditional NAS methods. Experimental results demonstrate significant reductions in search time and computational costs while maintaining or improving model accuracy.