Data reconstruction based on quantum neural networks

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

Fabrication and mechanical properties of Ti₂AlN/TiAl composite with continuous network structure

Nitrogen was introduced into TiAl pre-alloyed powder using high-temperature gas nitriding, and Ti2AlN/TiAl composites with a continuous network structure of the reinforced phase were prepared via spark plasma sintering. The results show that the hardness of the composite is significantly higher than that of TiAl alloy, and increases with the increase of nitriding time. The strengthening effect is originated from the synergistic effect of the solid-solution strengthening caused by the nitriding of the powder, the continuous network of the Ti2AlN phase with high hardness and elastic modulus, and the increase of dislocation density. Additionally, the compressive strength of the Ti2AlN/TiAl composites is lower than that of the TiAl alloy, which is related to a part of Ti2AlN particles that are directly formed after nitriding and excessive reinforcement content.</p