论文标题
基于量子神经网络的数据重建
Data reconstruction based on quantum neural networks
论文作者
论文摘要
从小型数据中重建大型数据是信息科学中的重要问题，一个典型的例子是计算机视觉中的图像超分辨率重建。将机器学习和量子计算结合在一起，量子机器学习表明了加速数据处理的能力，并为信息处理提供了新的方法。在本文中，我们提出了两个基于量子神经网络（QNN）和量子自动编码器（QAE）的数据重建框架。通过将MNIST手写数字作为数据集评估两个框架的效果。仿真结果表明，QNN和QAE可以很好地用于数据重建。我们还将结果与经典的超分辨率神经网络进行了比较，一个QNN的结果非常接近经典。
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.
