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Journal of Theoretical and Applied Information Technology

QUANTUM-ENHANCED FEATURE EXTRACTION (QUFEX): A CONCEPTUAL FRAMEWORK FOR PRECISION ATMOSPHERIC RIVER ANALYSIS

CHITRA P SIVACHITRALAKSHMI

Published Feb 28, 2026• 0 citations
Publisher

Abstract

Atmospheric Rivers (ARs) are major contributors to global hydrological variability and flood hazards, particularly in regions influenced by monsoonal systems. Accurate detection of ARs remains a challenge due to their dynamic, non-linear spatial patterns. This study presents a comparative evaluation of three advanced deep learning models—U-Net, CGNet, and DeepLabV3+—and proposes Q-ARNet, a quantum-enhanced atmospheric river detection framework based on a Quantum Neural Network (QNN) for image segmentation. The experiments were conducted on a dataset comprising 574 AR events from 1951 to 2020, incorporating variables such as integrated water vapor (IWV), zonal and meridional wind components (U850, V850), and sea-level pressure (PSL). Q-ARNet outperforms current classical benchmarks by approximately 3.5% in accuracy while utilizing a substantially reduced input feature set, thereby addressing the computational bottleneck associated with high-dimensional climate data. The proposed framework achieves an average accuracy of 97.5 ± 0.3%, an Intersection over Union (IoU) of 94.5 ± 0.4%, and an F1-score of 95.7 ± 0.2%, consistently surpassing classical deep learning models. These findings highlight the potential of quantum neural networks to enhance both the representational capacity and computational efficiency of atmospheric modeling through quantum parallelism and entanglement. The study contributes to the growing intersection of quantum computing and climate informatics, offering a promising framework for improved flood forecasting and climate risk management.

DOI
10.5281/zenodo.18823767

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Atmospheric Rivers (ARs) are major contributors to global hydrological variability and flood hazards, particularly in regions influenced by monsoonal systems. Accurate detection of ARs remains a challenge due to their dynamic, non-linear spatial patterns. This study presents a comparative evaluation of three advanced deep learning models—U-Net, CGNet, and DeepLabV3+—and proposes Q-ARNet, a quantum-enhanced atmospheric river detection framework based on a Quantum Neural Network (QNN) for image segmentation. The experiments were conducted on a dataset comprising 574 AR events from 1951 to 2020, incorporating variables such as integrated water vapor (IWV), zonal and meridional wind components (U850, V850), and sea-level pressure (PSL). Q-ARNet outperforms current classical benchmarks by approximately 3.5% in accuracy while utilizing a substantially reduced input feature set, thereby addressing the computational bottleneck associated with high-dimensional climate data. The proposed framework achieves an average accuracy of 97.5 ± 0.3%, an Intersection over Union (IoU) of 94.5 ± 0.4%, and an F1-score of 95.7 ± 0.2%, consistently surpassing classical deep learning models. These findings highlight the potential of quantum neural networks to enhance both the representational capacity and computational efficiency of atmospheric modeling through quantum parallelism and entanglement. The study contributes to the growing intersection of quantum computing and climate informatics, offering a promising framework for improved flood forecasting and climate risk management.
Weather forecasting plays a critical role across various sectors, supporting strategic planning and reducing the impact of hazardous climatic conditions. However, the inherently chaotic and nonlinear nature of atmospheric systems limits the performance of conventional forecasting approaches, often resulting in prediction inaccuracies and heightened risk. This study proposes a quantum-driven predictive framework that exploits the computational strengths of Quantum Machine Learning (QML) and hybrid quantum–classical optimization to enhance the accuracy and efficiency of modern forecasting models. The framework employs Quantum Neural Networks (QNNs) combined with Variational Quantum Circuits (VQCs) to learn complex spatial–temporal dynamics embedded within large meteorological datasets. Through quantum feature encoding, key atmospheric indicators—including temperature, humidity, wind velocity, and barometric pressure—are projected into high-dimensional Hilbert spaces, enabling more expressive pattern extraction and robust predictive behavior. Simulation results reveal that the quantum-augmented approach surpasses traditional deep learning architectures in both training convergence and forecast precision when applied to extensive weather records. The hybrid design further supports scalability by intelligently distributing computational workloads between classical processors and quantum hardware. Overall, this research demonstrates the transformative potential of quantum computing in atmospheric modeling, offering a foundation for future real-time forecasting systems capable of managing the increasing complexity of global climate patterns.
Reliable flood detection is critical for disaster management, yet classical deep learning models often struggle with the high-dimensional, nonlinear complexities inherent in remote sensing data. To mitigate these limitations, we introduced a novel Quantum-Enhanced Vision Transformer (ViT) that synergizes the global context-awareness of transformers with the expressive feature extraction capabilities of quantum computing. Using remote sensing imagery, we developed a hybrid architecture that processes inputs through parallel pathways, a ViT backbone and a quantum branch utilizing a 4-qubit parameterized quantum circuit for localized feature mapping. These distinct representations were fused to optimize binary classification. Results showed that the proposed hybrid model significantly outperformed a classical ViT baseline, increased overall accuracy from 84.48% to 94.47% and the F1-score from 0.841 to 0.944. Notably, the quantum integration substantially improved discriminative power in complex terrains for both class. These findings validate the potential of quantum-classical hybrid models to enhance precision in hydrological monitoring and earth observation applications.
Reliable flood detection is critical for disaster management, yet classical deep learning models often struggle with the high-dimensional, nonlinear complexities inherent in remote sensing data. To mitigate these limitations, we introduced a novel Quantum-Enhanced Vision Transformer (ViT) that synergizes the global context-awareness of transformers with the expressive feature extraction capabilities of quantum computing. Using remote sensing imagery, we developed a hybrid architecture that processes inputs through parallel pathways, a ViT backbone and a quantum branch utilizing a 4-qubit parameterized quantum circuit for localized feature mapping. These distinct representations were fused to optimize binary classification. Results showed that the proposed hybrid model significantly outperformed a classical ViT baseline, increased overall accuracy from 84.48 % to 94.47 % and the F1-score from 0.841 to 0.944. Notably, the quantum integration substantially improved discriminative power in complex terrains for both class. These findings validate the potential of quantum-classical hybrid models to enhance precision in hydrological monitoring and earth observation applications.
Abstract This study investigates the potential of quantum machine learning (QML) to improve flood forecasting. We focus on daily flood events along Germany’s Wupper River in 2023. Our approach combines classical machine learning (SVM, KNN, regression, AR models) with QML techniques (Adaboost, Quantum Variational Circuits, QBoost, QSV C_M L). This hybrid model leverages quantum properties like superposition and entanglement to achieve better accuracy and efficiency. Classical and QML models are compared based on training time, accuracy, and scalability. Results show that QML models offer competitive training times and improved prediction accuracy. This research signifies a step towards utilizing quantum technologies for climate change adaptation. We emphasize collaboration and continuous innovation to implement this model in real-world flood management, ultimately enhancing global resilience against floods.
Abstract We introduce quantum feature extraction (QuFeX), a novel quantum machine learning module. The proposed module enables feature extraction in a reduced-dimensional space, significantly decreasing the number of parallel evaluations required in typical quantum convolutional neural network (NNs) architectures. Its design allows seamless integration into deep classical NNs, making it particularly suitable for hybrid quantum–classical models. As an application of QuFeX, we propose Qu-Net—a hybrid architecture which integrates QuFeX at the bottleneck of a U-Net architecture. The latter is widely used for image segmentation tasks such as medical imaging and autonomous driving. Our numerical analysis indicates that the Qu-Net can achieve superior segmentation performance compared to a U-Net baseline. These results highlight the potential of QuFeX to enhance deep NNs by leveraging hybrid computational paradigms, providing a path towards a robust framework for real-world applications requiring precise feature extraction.
Typhoon trajectory forecasting is essential for disaster preparedness but remains computationally demanding due to the complexity of atmospheric dynamics and the resource requirements of deep learning models. Quantum-Train (QT), a hybrid quantum-classical framework that leverages quantum neural networks (QNNs) to generate trainable parameters exclusively during training, eliminating the need for quantum hardware at inference time. Building on QT's success across multiple domains, including image classification, reinforcement learning, flood prediction, and large language model (LLM) finetuning, we introduce Quantum Parameter Adaptation (QPA) for efficient typhoon forecasting model learning. Integrated with an Attention-based Multi-ConvGRU model, QPA enables parameterefficient training while maintaining predictive accuracy. This work represents the first application of quantum machine learning (QML) to large-scale typhoon trajectory prediction, offering a scalable and energy-efficient approach to climate modeling. Our results demonstrate that QPA significantly reduces the number of trainable parameters while preserving performance, making high-performance forecasting more accessible and sustainable through hybrid quantum-classical learning.