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IEEE International Conference on Quantum Photonics, Artificial Intelligence & Networking (QPAIN)

Weather State Classification Using Quantum Machine Learning Algorithms

Sujoy Kumar Ray, Ashis Kumar Mandal, Oishik Biswas, Md. Shofiqul Islam, Omar Shehab

Published Apr 16, 2026• 0 citations
Publisher

Abstract

Weather forecasting is a major challenge because of the unpredictable nature of atmospheric systems and the large number of interacting variables. Although traditional machine learning models have been used for weather prediction, they cannot capture the complex, nonlinear dynamics of weather. Recently, Quantum Machine Learning (QML) shows ability to improve computational speed and pattern recognition. Its practical application in weather prediction, however, is not well exploited. This work methodically analyzes QML algorithms for predicting weather states and compares their predictive performance with that of classical machine learning. We assembled extensive hourly meteorological data for Sylhet City, Bangladesh ($24.9172^{\circ} \mathrm{N}, 91.8319^{\circ}$E) over 185 days from Microsoft and IBM weather sites, totaling 4,504 data points. The dataset combines the main atmospheric parameters as temperature, real feel temperature, humidity, precipitation probability, visibility, dew point, and air pressure, from Microsoft and IBM weather sites. Four QML algorithms, including Quantum Random Forest (QRF), Quantum Decision Tree (QDT), Quantum Support Vector Machine (QSVM), and Quantum Neural Network (QNN) are trained and tested for five weather state classification, and compared to their classical counterparts named SVM, Random Forest, Decision Tree, and Neural Network. All quantum experiments were run on Qiskit simulators due to current hardware limitations. Results show that classical models performed slightly better than quantum models, and the difference was not very large.

DOI
10.1109/qpain69676.2026.11546024

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Quantum Machine Learning (QML) presents as a revolutionary approach to weather forecasting by using quantum computing to improve predictive modeling capabilities. In this study, we apply QML models, including Quantum Gated Recurrent Units (QGRUs), Quantum Neural Networks (QNNs), Quantum Long Short-Term Memory(QLSTM), Variational Quantum Circuits(VQCs), and Quantum Support Vector Machines(QSVMs), to analyze meteorological time-series data from the ERA5 dataset. Our methodology includes preprocessing meteorological features, implementing QML architectures for both classification and regression tasks. The results demonstrate that QML models can achieve reasonable accuracy in both prediction and classification tasks, particularly in binary classification. However, challenges such as quantum hardware limitations and noise affect scalability and generalization. This research provides insights into the feasibility of QML for weather prediction, paving the way for further exploration of hybrid quantum-classical frameworks to enhance meteorological forecasting.
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This paper extends the idea of creating a Quantum Machine Learning classifier and applying it to real weather data from the weather station behind the Pa Sak Jonlasit Dam.A systematic study of classical features and optimizers with different iterations of parametrized circuits is presented.The study of the weather behind the dam is based on weather data from 2016 to 2022 as a training dataset.Classification is one problem that can be effectively solved with quantum gates.There are several types of classifiers in the quantum domain, such as Quantum Support Vector Machine (QSVM) with kernel approximation, Quantum Neural Networks (QNN), and Variational Quantum Classification (VQC).According to the experiments conducted using Qiskit, an open-source software development kit developed by IBM, Quantum Support Vector Machine (QSVM), Quantum Neural Network (QNN), and Variable Quantum Classification (VQC) achieved accuracy 85.3%, 52.1%, and 70.1% respectively.Testing their performance on a test dataset would be interesting, even in these small examples.
Quantum Machine Learning (QML) has emerged as a paradigm that combines the computational advantages of quantum computing with the predictive capabilities of machine learning to address complex data classification problems. As data dimensionality increases rapidly and classical learning algorithms face scalability constraints, QML leverages quantum parallelism, entanglement, and high-dimensional Hilbert space representations to enhance learning performance. This paper reviews and analyses advanced QML algorithms, including Quantum Support Vector Machines (QSVM), Variational Quantum Circuits (VQC), Quantum Neural Networks (QNN), and Quantum Kernel Estimation, for optimizing both binary and multi-class classification tasks under high-complexity conditions. The proposed QML framework is evaluated on benchmark datasets like MNIST, the Breast Cancer Wisconsin (BCW) dataset, and synthetic nonlinear datasets, and is compared against classical machine learning baselines, including Support Vector Machines (SVM), Random Forests (RF), and Deep Neural Networks (DNN). The results demonstrate notable improvements in classification accuracy (up to 96.8%), decision margins, and computational efficiency in quantum-suitable data regimes, while also highlighting current limitations related to noise, circuit depth, and hardware constraints. Overall, the study presents a unified QML framework, theoretical formulations, and experimental evaluations that illustrate the potential of quantum algorithms for next-generation classification tasks.
Quantum Machine Learning (QML):An emerging research area that integrates principles of quantum computing with machine learning algorithms to enhance computational efficiency, scalability, and learning capability for complex data-driven problems. Cloud-Based Quantum Computing:A computing paradigm that provides remote access to quantum processors and simulators through cloud platforms, enabling scalable, cost-effective, and accessible quantum experimentation without the need for dedicated quantum hardware. Hybrid Quantum–Classical Models:Machine learning architectures that combine classical data preprocessing and optimization techniques with quantum circuits to leverage the strengths of both classical and quantum computation. Weather Forecasting:The process of predicting atmospheric conditions such as temperature, humidity, pressure, and wind speed using data-driven models and computational techniques. Variational Quantum Circuits (VQC):Parameterized quantum circuits optimized using classical algorithms, widely used in quantum machine learning for classification and regression tasks on Noisy Intermediate-Scale Quantum (NISQ) devices. NISQ Devices:Current-generation quantum computers characterized by limited qubit counts and noise, which influence the performance and accuracy of present-day quantum algorithms.