Fig. 5
From: Deep quanvolutional neural networks with enhanced trainability and gradient propagation
Detailed Methodology. A comprehensive overview of steps involved in the analysis of trainable QuNNs and proposed ResQuNNs. The results of quanvolutional layers are postprocess using both classical layer and quantum circuit. Zero Padding is performed to match the dimensions of input and output before passing it to residual block. The gradients accesibility through all the layers and trainaing and validation accuracy are used as eveluation metrics. QCL quanvolutional layer.
