Quantum-Inspired Neural Networks: A Paradigm Shift in AI Computation
Abstract
Quantum computing has shown immense potential in revolutionizing computational efficiency. This paper explores the integration of quantum-inspired algorithms with deep neural networks to enhance learning efficiency and problem-solving capabilities. We propose a novel quantum-inspired backpropagation mechanism that leverages quantum superposition principles to optimize weight updates. Experimental results demonstrate improved convergence rates and reduced computational complexity compared to classical deep learning models. The findings suggest a promising direction for bridging the gap between quantum computing and artificial intelligence.
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