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International Conference on Machine Intelligence and Smart Innovation (ICMISI)

Comparative Analysis of Neural Network and Quantum Neural Network for Enhanced Air Quality Forecasting

Noha A. Sobhi, Doaa A. Shoieb, Sherin Moustafa Youssef

Published May 10, 2025• 1 citation
Publisher

Abstract

Air pollution forecasting is a critical challenge in environmental science. Nitrogen Dioxide (NO2) and Carbon Monoxide (CO) are two of the most harmful gases, contributing significantly to respiratory illnesses and environmental degradation. Accurate predictions of NO2and CO levels help mitigate the adverse effects of pollutants on public health. A hybrid quantum-classical machine learning model is developed, integrating a feature map, variational ansatz, and quantum regressor optimized with multiple optimizers. The results are compared with classical machine learning model, such as Multi-Layer Perceptron (MLP), demonstrating the potential of quantum computing in predictive modeling. Experimental results using the air quality dataset reveal that the proposed model achieves superior performance compared to traditional approaches, as measured by MSE, RMSE, MAE, and R2. The findings suggest that quantum-enhanced neural networks provide a promising avenue for real-time air pollution forecasting, enabling proactive pollution control measures.

DOI
10.1109/icmisi65108.2025.11115298

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Air pollution forecasting is a critical challenge in environmental science. Nitrogen Dioxide (NO2) and Carbon Monoxide (CO) are two of the most harmful gases, contributing significantly to respiratory illnesses and environmental degradation. Accurate predictions of NO2and CO levels help mitigate the adverse effects of pollutants on public health. A hybrid quantum-classical machine learning model is developed, integrating a feature map, variational ansatz, and quantum regressor optimized with multiple optimizers. The results are compared with classical machine learning model, such as Multi-Layer Perceptron (MLP), demonstrating the potential of quantum computing in predictive modeling. Experimental results using the air quality dataset reveal that the proposed model achieves superior performance compared to traditional approaches, as measured by MSE, RMSE, MAE, and R2. The findings suggest that quantum-enhanced neural networks provide a promising avenue for real-time air pollution forecasting, enabling proactive pollution control measures.
Abstract Our study proposes the MDQ-3L framework, a multi-metric, quantum-aware hierarchy for benchmarking classical machine learning, deep learning, and quantum machine learning models in air quality prediction. PM2.5, PM10, NO, NO2, NOx, NH3, CO, SO2, O3, Benzene, Toluene, and Xylene were considered predictors of AQI. Seventeen ML regressors, nine DL models, and seven QML circuits were evaluated using MAE, MSE, RMSE, R², RMSLE, MAPE, and QML-specific expressibility (0.739-2.0) and entangling power (0-0.398). Aggregated via min-max normalization and weights, the framework penalizes negative R² and high structural costs, yielding CatBoost and DBN as top performers, while QML trailed by 25–40% in composite scores due to generalization gaps and barren plateau risks. MDQ-3L aligns circuits to targets, reducing QCNN/TTN penalties by ~ 30% relative to non-benchmarked rankings. Our approach exposes QML limitations under NISQ constraints (4–6 qubits, shallow depth), supporting low-entanglement ansätze like SELR/HEA for 10–15% potential gains, and provides an interpretable tool for hybrid environmental modeling, advancing Quantum Machine Intelligence benchmarks.
Air pollution poses a significant threat to public health, exacerbated by the rapid industrialization and urbanization of modern society. While traditional deep learning offers predictive capabilities, recent advancements in quantum machine learning (QML) present a compelling alternative. Here, we introduce a scalable hybrid framework that integrates variational quantum circuits with quantum annealing. Leveraging a neuromorphic quantum computing platform to transcend the qubit limitations of the NISQ era, we model particulate matter (PM2.5_{2.5}2.5​) dynamics using high-density urban data from the UK. We demonstrate that our Quantum Neural Networks (QNNs) exhibit an intrinsic resilience to input volatility, significantly outperforming classical baselines (MLP and LSTM) on raw, high-entropy environmental data. Furthermore, we successfully scaled the model to 172 qubits, observing a synergistic enhancement when coupled with wavelet decomposition that achieves state-of-the-art predictive fidelity. These findings establish a critical benchmark for quantum utility, proving that scalable, neuromorphic quantum machine learning can now address complex, non-linear real-world problems.
In a world burdened by air pollution, the integration of state-of-the-art sensor calibration techniques utilizing Quantum Computing (QC) and Machine Learning (ML) holds promise for enhancing the accuracy and efficiency of air quality monitoring systems in smart cities. This article investigates the process of calibrating inexpensive optical fine-dust sensors through advanced methodologies such as Deep Learning (DL) and Quantum Machine Learning (QML). The objective of the project is to compare four sophisticated algorithms from both the classical and quantum realms to discern their disparities and explore possible alternative approaches to improve the precision and dependability of particulate matter measurements in urban air quality surveillance. Classical Feed-Forward Neural Networks (FFNN) and Long Short-Term Memory (LSTM) models are evaluated against their quantum counterparts: Variational Quantum Regressors (VQR) and Quantum LSTM (QLSTM) circuits. Through meticulous testing, including hyperparameter optimization and cross-validation, the study assesses the potential of quantum models to refine calibration performance. Our analysis shows that: the FFNN model achieved superior calibration accuracy on the test set compared to the VQR model in terms of lower L1 loss function (2.92 vs 4.81); the QLSTM slightly outperformed the LSTM model (loss on the test set: 2.70 vs 2.77), despite using fewer trainable weights (66 vs 482).
The essence of quantum machine learning is to optimize problem-solving by executing machine learning algorithms on quantum computers and exploiting potent laws such as superposition and entanglement. Support vector machine (SVM) is widely recognized as one of the most effective classification machine learning techniques currently available. Since, in conventional systems, the SVM kernel technique tends to sluggish down and even fail as datasets become increasingly complex or jumbled. To compare the execution time and accuracy of conventional SVM classification to that of quantum SVM classification, the appropriate quantum features for mapping need to be selected. As the dataset grows complex, the importance of selecting an appropriate feature map that outperforms or performs as well as the classification grows. This paper utilizes conventional SVM to select an optimal feature map and benchmark dataset for predicting air quality. Experimental evidence demonstrates that the precision of quantum SVM surpasses that of classical SVM for air quality assessment. Using quantum labs from IBM's quantum computer cloud, conventional and quantum computing have been compared. When applied to the same dataset, the conventional SVM achieved an accuracy of 91% and 87% respectively, whereas the quantum SVM demonstrated an accuracy of 97% and 94% respectively for air quality prediction. The study introduces the use of quantum Support Vector Machines (SVM) for predicting air quality. It emphasizes the novel method of choosing the best quantum feature maps. Through the utilization of quantum-enhanced feature mapping, our objective is to exceed the constraints of classical SVM and achieve unparalleled levels of precision and effectiveness. We conduct precise experiments utilizing IBM's state-of-the-art quantum computer cloud to compare the performance of conventional and quantum SVM algorithms on a shared dataset.
Despite the growing impact of emissions on our health and the environment, there remains an unmet need for emission concentration prediction and forecasting. The accumulating monitoring station and satellite data make the problem well-suited for quantum machine learning. This work takes a quantum machine learning approach to the spatiotemporal prediction of emission concentration. A quantum quanvolutional neural network model was developed and compared to a classical spatiotemporal ConvLSTM model using an evaluation framework of baseline models and metrics of per-pixel loss and intersection over union accuracy. The quantum quanvolutional neural network developed successfully generates one-hour-ahead emission concentration forecasts with increasingly lower loss (6.5% and 30.5% less) and higher accuracy (18.4% and 18.6% higher) compared to the input-independent and random baselines at the end of training. The quantum model was also comparable to the classical ConvLSTM model, with slightly lower loss (4%) but also slightly lower accuracy (3.7%). The study’s results suggest that the quantum machine learning approach has the potential to improve emission concentration modeling and could become a powerful tool for accurately predicting air pollution.
Abstract Quantum Machine Learning (QML) is an emerging field at the intersection of quantum computing and machine learning that aims to enhance classical learning methods by leveraging principles such as superposition and entanglement. However, skepticism remains regarding its practical advantages, particularly due to the limitations of noisy intermediate-scale quantum (NISQ) devices. This study addresses these concerns through a systematic evaluation of Quantum Neural Networks (QNNs), quantum-inspired counterparts of Artificial Neural Networks (ANNs). We construct and analyze 12 distinct QNN configurations by combining two quantum feature maps with six entanglement strategies for ansatz design. Experiments conducted on a wind energy dataset show that QNNs can achieve competitive or superior performance compared to classical baseline models, with the best-performing configuration (QNN-3) reaching an R 2 score of up to 0.94 when forecasting wind power output using four input features. In addition, we provide a detailed analysis of simulation time across different configurations, highlighting trade-offs between model complexity and computational cost. These findings demonstrate the potential of QNNs as viable models for predictive tasks and contribute to the ongoing evaluation of QML in real-world applications.