Forecasting weather is critical in different application sectors and affects decision-making and resource distribution. Long Short-Term Memory (LSTM) models are popular, but they can be improved even further with the use of quantum computing techniques. This paper discusses a Quantum Long Short-Term Memory (QLSTM) simulated in PennyLane-based simulators. According to the experimental results, the suggested QLSTM model produced a mean absolute error (MAE) of 0.009066, a test mean square error (MSE) of 0.000162, and a coefficient of determination (R2) of 0.999846, surpassing both quantum models and classical baselines. These results highlight the potential of quantum-assisted architectures to improve accuracy in weather prediction. The research advances weather forecasting and contributes to quantum computing applications, guiding researchers in developing precise weather prediction models and enhancing decision-making in weather-sensitive domains. The proposed 4-qubit quantum model was tested with noise models, and it demonstrated high learning efficiency and strong robustness against realistic NISQ-level noise, maintaining stable optimization and low error even under moderate perturbations. • Quantum LSTM Development: The classical LSTM was extended into the quantum domain using Variational Quantum Circuits (VQCs) for feature extraction and data compression, applied to weather prediction tasks. • Experimental Evaluation: Three QLSTM variants (4, 6, and 8 qubit) were tested on two weather datasets and compared with classical LSTM and state-of-the-art QLSTM models. • Performance and Robustness: The QLSTM models outperformed baseline models, with the 4-qubit model showing strong learning efficiency and robustness under realistic NISQ-level noise.
Forecasting weather is critical in different application sectors and affects decision-making and resource distribution. Long Short-Term Memory (LSTM) models are popular, but they can be improved even further with the use of quantum computing techniques. This paper discusses a Quantum Long Short-Term Memory (QLSTM) simulated in PennyLane-based simulators. According to the experimental results, the suggested QLSTM model produced a mean absolute error (MAE) of 0.009066, a test mean square error (MSE) of 0.000162, and a coefficient of determination (R2) of 0.999846, surpassing both quantum models and classical baselines. These results highlight the potential of quantum-assisted architectures to improve accuracy in weather prediction. The research advances weather forecasting and contributes to quantum computing applications, guiding researchers in developing precise weather prediction models and enhancing decision-making in weather-sensitive domains. The proposed 4-qubit quantum model was tested with noise models, and it demonstrated high learning efficiency and strong robustness against realistic NISQ-level noise, maintaining stable optimization and low error even under moderate perturbations. • Quantum LSTM Development: The classical LSTM was extended into the quantum domain using Variational Quantum Circuits (VQCs) for feature extraction and data compression, applied to weather prediction tasks. • Experimental Evaluation: Three QLSTM variants (4, 6, and 8 qubit) were tested on two weather datasets and compared with classical LSTM and state-of-the-art QLSTM models. • Performance and Robustness: The QLSTM models outperformed baseline models, with the 4-qubit model showing strong learning efficiency and robustness under realistic NISQ-level noise.
We present a quantum-enhanced deep learning framework for short-term forecasting of extreme convective weather systems, specifically targeting the formation of bow echo structures through prediction clouds. Our approach integrates a quantum convolutional neural network (QCNN) into a classical convolutional neural network-long short-term memory (CNN-LSTM) pipeline, incorporating a 10-qubit variational quantum circuit executed on the lightning.qubit simulator. The model is trained using heterogeneous meteorological data (satellite images, convective available potential energy (CAPE) fields, lightning activity) from the extreme storm that hit the Corsican coast on August 18, 2022, preceding the convective event and evaluated during the critical intensification window (0700–0800 h). We compare this hybrid quantum model with classical CNN-LSTM and CNN-LSTM-Transformer architectures using three complementary metrics: Root mean squared error (RMSE), structural similarity index (SSIM), and Wasserstein distance. The quantum model demonstrates superior robustness to input noise and adversarial perturbations fast gradient sign method (FGSM), maintaining spatial coherence and stable prediction under degradation. These findings highlight the capacity of quantum circuits to encode more resilient representations for meteorological inference. This study provides a concrete application of hybrid QCNN architectures in the domain of extreme weather forecasting and lays the groundwork for future integration of topological data analysis to assess the preservation of topological features in quantum predictions.
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.
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.
Quantum long short-term memory (QLSTM) models extend recurrent sequence learning with variational quantum circuits, but their optimization behavior can vary substantially across random initializations and temporal contexts. This paper evaluates a recursive QLSTM architecture against a standard QLSTM for one-step-ahead prediction of daily minimum and maximum temperature. Using daily weather observations from Toronto and identical training settings, we compare convergence, predictive accuracy, and generalization across input windows of 8, 16, and 32 days over 20 random seeds. The recursive model consistently reaches a near-optimal test loss earlier, reduces mean absolute error and root mean squared error, and exhibits a smaller generalization gap. These results indicate that recursive quantum feature transformations can improve stability and out-of-sample performance for compact hybrid quantum--classical temporal models.
Abstract Hybrid quantum recurrent models are commonly evaluated against compact classical networks, making it diffcult to determine whether observed gains arise from the variational quantum circuit (VQC) or from the surrounding recurrent architecture. This study presents an architecture and parameter-controlled evaluation of quantum gated recurrent unit (QGRU) and quantum long short-term memory (QLSTM) models for one-step-ahead meteorological forecasting. Each quantum model is compared with both a compact, approximately parameter-matched recurrent baseline and an exact classical gate-core surrogate that preserves the hidden width, recurrent equations, gate-specific pre-/post-projections, optimization protocol, and total trainable-parameter count. All models use 17 input features, 24-step histories, a chronological 80/10/10 train–validation–test split, trainingonly standardization, and ten matched random seeds. QGRU achieves an RMSE of 1.1045 ± 0.0693, reducing test RMSE by 19.01% relative to the compact GRU and by 5.47% relative to its exact 773-parameter surrogate. It also attains a lower mean best validation MSE, reaches 95% of its achievable validation improvement after 5.5 ± 0.5 epochs, and exhibits a smaller final train–validation gap. QLSTM reduces test RMSE by 24.80% relative to the compact LSTM, while its exact 1,025-parameter surrogate achieves comparable predictive performance with slightly greater validation stability. Analysis of 1,949 epoch-level records across convergence, checkpoint quality, early stopping, generalization, and runtime demonstrates that VQC utility depends on the recurrent architecture in which it is embedded. All experiments are conducted using ideal noiseless statevector simulation.
Time series forecasting involves analyzing historical data to predict future trends and is useful for reducing uncertainty in various phenomena. Recurrent Neural Network-based Long Short-Term Memory (LSTM) has been shown to be used for time series forecasting. However, the introduction of a hybrid quantum-classical model of LSTM, i.e., Quantum LSTM (QLSTM), opens up avenues for faster and more efficient computing and, therefore, is of significant potential. These potentials underscore the necessity for a comparative analysis of both models to assess their effectiveness and efficiency. This study evaluates the performance of QLSTM and classical LSTM models in time series forecasting. Through controlled parametric numerical experiments, the study examines the performance of both models using CPUs and GPUs in cloud environments. Our findings reveal significant improvements in the QLSTM model, with enhanced training accuracy and reduced test loss compared to the classical LSTM under similar epochs and batch sizes. The results demonstrate the superior efficiency of the QLSTM model in handling large, complex data sets compared to the classical LSTM model.