Quantum-inspired neural networks for time-series air pollution prediction and control of the most polluted region in the world

作     者：Ahmad, Naushad Jas, Shubham 

作者机构：Mahatma Gandhi Cent Univ Comp Sci & Informat Technol Motihari 845401 Bihar India 

出 版 物：《QUANTUM MACHINE INTELLIGENCE》 (Quantum Mach. Intell.)

年 卷 期：2025年第7卷第1期

页      面：1-13页

核心收录：

基　　金：Mahatma Gandhi Central University 

主　　题：Quantum algorithms Dequantized algorithms Quantum-inspired algorithms Quantum convolutional neural network Quantum temporal convolutional network 

摘      要：Researchers in the field of quantum computing are primarily focused on quantum-inspired algorithms, which function effectively on classical computers while incorporating quantum principles. A significant challenge in heavily polluted areas is accurately predicting air pollution levels. For our study, we selected New Delhi, specifically the Anand Vihar air pollution station, known for its high concentrations of PM2.5\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$_{2.5}$$\end{document}. We proposed an approach to predict PM2.5\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$_{2.5}$$\end{document} levels based on various pollutant and meteorological parameters. Our model is an improved version of the quantum temporal convolutional network (QTCN), enhancing the traditional quantum convolutional neural network (QCNN) model. To evaluate our model s performance, we used several metrics, including mean squared error (MSE), root mean squared error (RMSE), mean absolute percentage error (MAPE), mean absolute error (MAE), and the coefficient of determination (R2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$2$$\end{document} score). Our proposed model achieved MAE and MAPE values of (59.031 +/- 1.23\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$59.031 \pm 1.