A Tensor Residual Circuit Neural Network Factorized with Matrix Product Operation

The recent introduction of the Tensor Circuit Neural Network (TCNN) marks a significant advancement in neural network design, addressing the ongoing challenge of balancing complexity with performance. By integrating the strengths of tensor neural networks and residual circuit models, the TCNN achieves notable improvements in generalization and robustness, crucial for real-world applications. Experimental results indicate that TCNN outperforms state-of-the-art models by 2%-3% in accuracy across various datasets, showcasing its potential to enhance feature learning efficiency. Furthermore, its ability to maintain performance under noise attacks highlights its robustness, making it a promising candidate for future AI applications. This development not only contributes to the field of artificial intelligence but also sets a new standard for the design of neural networks that prioritize both efficiency and reliability.