Paper ID: 2403.05754 • Published Mar 9, 2024
Hybrid Quantum-inspired Resnet and Densenet for Pattern Recognition
Andi Chen, Hua-Lei Yin, Zeng-Bing Chen, Shengjun Wu
TL;DR
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In this paper, we propose two hybrid quantum-inspired neural networks with
residual and dense connections respectively for pattern recognition. We explain
the concrete frameworks and illustrate the potential superiority to prevent
gradient explosion of our hybrid models. A group of numerical experiments about
generalization power shows that our hybrid models possess the same
generalization power as the pure classical models with different noisy datasets
utilized. More importantly, another group of numerical experiments of
robustness demonstrates that our hybrid models outperform pure classical models
notably in resistance to parameter attacks with various asymmetric noises.
Also, an ablation study indicate that the recognition accuracy of our hybrid
models is 2\%-3\% higher than that of the quantum neural network without
residual or dense connection. Eventually, we discuss the application scenarios
of our hybrid models by analyzing their computational complexities.