The performance of single image super-resolution reconstruction based on standard convolution is limited by the redundancy of the stacked network layers, making it difficult to implement the algorithm on the ground. Moreover, the single residual structure of the feature extraction layer cannot efficiently utilize the feature information obtained from convolution. To address these, this paper proposes a residual distillation reuse module based on the existing residual distillation-based structure to reduce the high-frequency information of the image lost in the residual distillation process. In addition, the base residual block is replaced by a blueprint separable convolution to decouple the spatial correlation of the feature map, such that the weight of highly correlated features can be reduced. As a result, the efficiency of convolution can be improved and the number of parameters can be reduced. We conduct comparative experiments on standard datasets such as Set5 to evaluate the proposed algorithm. The experimental results show that the peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) of the proposed algorithm can be improved by approximately 0.06~0.25 dB and 0.004~0.012, respectively, over the lightweightresidual distillation image super-resolution networks.

With the rapid advancement of quantum computing and information technology, cloud-based quantum machine learning has emerged as a promising solution, enabling resource-constrained users to perform quantum machine learning tasks via remote quantum servers while ensuring privacy protection for both data and models. A relatively comprehensive overview of the latest developments in this field is provided, starting from the fundamental theories of quantum inner products and variational quantum algorithms. An analysis is conducted on the implementation details and application examples of various cloud-based quantum machine learning methods based on quantum inner products and cloud-based variational quantum algorithms. Additionally, the challenges faced by current technologies are discussed and insights into future research directions are offered.