论文标题
量子量子回路和量子反向传播多层的比较研究
Comparative study of variational quantum circuit and quantum backpropagation multilayer perceptron for COVID-19 outbreak predictions
论文作者
论文摘要
有许多量子神经网络模型已应用于图像分类，模式识别等各种问题等。最近，在NISQ时代，混合量子古典模型显示出令人鼓舞的结果。多功能回归是古典机器学习中的常见问题。因此，我们介绍了连续可变量子神经网络（变化电路）和量子反向传播多层感知器（QBMLP）的比较分析。我们选择了当代的问题，即预测印度和美国的Covid-19案件的增加。我们提供了两个模型之间的统计比较，两种模型的性能都比经典的人工神经网络更好。
There are numerous models of quantum neural networks that have been applied to variegated problems such as image classification, pattern recognition etc.Quantum inspired algorithms have been relevant for quite awhile. More recently, in the NISQ era, hybrid quantum classical models have shown promising results. Multi-feature regression is common problem in classical machine learning. Hence we present a comparative analysis of continuous variable quantum neural networks (Variational circuits) and quantum backpropagating multi layer perceptron (QBMLP). We have chosen the contemporary problem of predicting rise in COVID-19 cases in India and USA. We provide a statistical comparison between two models , both of which perform better than the classical artificial neural networks.
