Air quality prediction based on quantum activation function optimized hybrid quantum classical neural network

Abstract

Accurate prediction of air quality index is a challenging task, in order to solve the gradient problem of traditional neural network methods in the time series prediction process as well as to improve the prediction accuracy, the study proposes a hybrid quantum neural network prediction model based on quantum activation function. The model utilizes a quantum classical convolutional neural network to tap into spatial correlations between different time periods and combines it with a quantum activation function so as to better avoid the gradient problem and solve the death RELU problem for better spatial feature extraction, and then uses the long short term memory neural network to capture the observations at different times. Experiments were conducted on different air quality datasets using the model, which proved that the proposed quantum activation function optimized hybrid quantum neural network algorithm showed more remarkable advantages in prediction accuracy than other model algorithms.