Hybrid Quantum-inspired Resnet and Densenet for Pattern Recognition
<|reference_start|>Hybrid Quantum-inspired Resnet and Densenet for Pattern Recognition: 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.<|reference_end|>
@article{chen2024hybrid,
    title={Hybrid Quantum-inspired Resnet and Densenet for Pattern Recognition},
    author={Andi Chen, Hua-Lei Yin, Zeng-Bing Chen, Shengjun Wu},
    journal={arXiv preprint arXiv:2403.05754},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.05754},
    primaryClass={cs.LG cs.ET}
}