Hybrid Quantum-inspired Resnet and Densenet for Pattern Recognition with
  Completeness Analysis
Andi Chen Hua-Lei Yin Zeng-Bing Chen Shengjun Wu
With the contemporary digital technology approaching, deep neural networks
are emerging as the foundational algorithm of the artificial intelligence boom.
Whereas, the evolving social demands have been emphasizing the necessity of
novel methodologies to substitute traditional neural networks. Concurrently,
the advent of the post-Moore era has spurred the development of
quantum-inspired neural networks with outstanding potentials at certain
circumstances. Nonetheless, a definitive evaluating system with detailed
metrics is tremendously vital and indispensable owing to the vague indicators
in comparison between the novel and traditional deep learning models at
present. Hence, to improve and evaluate the performances of the novel neural
networks more comprehensively in complex and unpredictable environments, we
propose two hybrid quantum-inspired neural networks which are rooted in
residual and dense connections respectively for pattern recognitions with
completeness representation theory for model assessment. Comparative analyses
against pure classical models with detailed frameworks reveal that our hybrid
models with lower parameter complexity not only match the generalization power
of pure classical models, but also outperform them notably in resistance to
parameter attacks with various asymmetric noises. Moreover, our hybrid models
indicate unique superiority to prevent gradient explosion problems through
theoretical argumentation. Eventually, We elaborate on the application
scenarios where our hybrid models are applicable and efficient, which paves the
way for their industrialization and commercialization.