  Reconstruction of large-sized data from small-sized ones is an important
problem in information science, and a typical example is the image
super-resolution reconstruction in computer vision. Combining machine learning
and quantum computing, quantum machine learning has shown the ability to
accelerate data processing and provides new methods for information processing.
In this paper, we propose two frameworks for data reconstruction based on
quantum neural networks (QNNs) and quantum autoencoder (QAE). The effects of
the two frameworks are evaluated by using the MNIST handwritten digits as
datasets. Simulation results show that QNNs and QAE can work well for data
reconstruction. We also compare our results with classical super-resolution
neural networks, and the results of one QNN are very close to classical ones.
