Fig. 4

Comparison of two image classification frameworks. a Classical framework. The input images are transformed by a backbone model into relevant features. The following global pooling module downsamples these feature maps and outputs a fix-length vector which is fed into fully connected (FC) layers for the final classification. b Proposed framework. The global pooling module is replaced by a variational quantum circuit with amplitude encoding which we denote by AE-VQC. The feature maps extracted by the backbone model are directly fed into this AE-VQC without dimensionality reduction. The outputs of the AE-VQC are transformed into classification results via FC layers