Hybrid Quantum-inspired Resnet and Densenet for Pattern Recognition

• URL: http://arxiv.org/abs/2403.05754v6
• Date: Tue, 18 Feb 2025 08:03:59 GMT
• Title: Hybrid Quantum-inspired Resnet and Densenet for Pattern Recognition
• Authors: Andi Chen, Hua-Lei Yin, Zeng-Bing Chen, Shengjun Wu,
• Abstract summary: We propose two hybrid quantum-inspired neural networks with adaptive residual and dense connections respectively for pattern recognition. We show the potential superiority of our hybrid models to prevent gradient explosion owing to the quantum-inspired layers.
• Score: 1.0499611180329804
• License:
• Abstract: In this paper, we propose two hybrid quantum-inspired neural networks with adaptive residual and dense connections respectively for pattern recognition. We explain the frameworks of the symmetrical circuit models in the quantum-inspired layers in our hybrid models. We also illustrate the potential superiority of our hybrid models to prevent gradient explosion owing to the quantum-inspired layers. Groups of numerical experiments on generalization power show that our hybrid models possess roughly the same level of generalization power as the pure classical models with different noisy datasets utilized. Furthermore, the comparison on generalization ability between our hybrid models and a state-of-the-art hybrid quantum-classical convolutional network demonstrates 3%-4% higher accuracy of our hybrid densely-connected model than the hybrid quantum-classical network. Simultaneously, in terms of groups of experiment on robustness, the results demonstrate that our two hybrid models outperform pure classical models notably in resistance to parameter attacks with various asymmetric noises. They also indicate the superiority of our densely-connected hybrid model over the hybrid quantum-classical network under both symmetrical and asymmetrical attacks. Furthermore, an ablation study indicate that the recognition accuracy of our two hybrid models is 2%-3% higher than that of the traditional quantum-inspired neural network without residual or dense connection. Eventually, we discuss the application scenarios of our hybrid models by analyzing their computational complexities.

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