Effective weather forecasting for cyclones is crucial for minimizing harm to both people and the environment. Accurate estimation of tropical cyclone (TC) intensity is essential for disaster prevention. Although convolutional neural networks (CNNs) have improved this process, they often struggle to capture global spatial relationships in images. Quantum Image Processing (QIP) leverages quantum computing advantages but faces challenges such as noise and hardware limitations. This study represents the first effort to estimate tropical cyclone intensity prediction using two popular quantum image representation formats: Flexible Representation of Quantum Images (FRQI) and a Novel Enhanced Quantum Representation (NEQR), as data encoders in Quantum Convolutional Neural Networks (QCNN) utilizing INSAT 3D satellite images. By employing TCs from 2012 to 2021 as training data, the model achieved an overall mean square error (MSE) of 0.0384 for FRQI and 0.0002 for NEQR. The findings indicate that NEQR significantly outperforms FRQI in cyclone image prediction.
Effective weather forecasting for cyclones is crucial for minimizing harm to both people and the environment. Accurate estimation of tropical cyclone (TC) intensity is essential for disaster prevention. Although convolutional neural networks (CNNs) have improved this process, they often struggle to capture global spatial relationships in images. Quantum Image Processing (QIP) leverages quantum computing advantages but faces challenges such as noise and hardware limitations. This study represents the first effort to estimate tropical cyclone intensity prediction using two popular quantum image representation formats: Flexible Representation of Quantum Images (FRQI) and a Novel Enhanced Quantum Representation (NEQR), as data encoders in Quantum Convolutional Neural Networks (QCNN) utilizing INSAT 3D satellite images. By employing TCs from 2012 to 2021 as training data, the model achieved an overall mean square error (MSE) of 0.0384 for FRQI and 0.0002 for NEQR. The findings indicate that NEQR significantly outperforms FRQI in cyclone image prediction.
This research addresses the critical challenges of cyclone analysis and prediction, aiming to significantly advance the accuracy and efficiency of our understanding of cyclone behavior. Cyclones are powerful and destructive weather phe-nomena that can cause significant damage to life and property. Accurately predicting their intensity and potential impact is vital for timely evacuations and mitigation strategies. Current classical CNN models often struggle with classifying cyclones, mainly when their intensities exhibit subtle differences. Leveraging the NRLMRY (Naval Research Laboratory Monterey) Tropical Cyclone Data (TCDAT), we employ Quantum Machine Learning (QML) to enhance cyclone intensity estimation. By adopting a Quantum-Classical hybrid approach, which harnesses the power of quantum computing principles, we seek to overcome these limitations, providing more precise intensity estimations. Beyond intensity estimation, this project extends its focus to predicting the potential severity of damage that cyclones may inflict on cyclone-prone regions. Utilizing the same quantum-powered approach, we aspire to accurately classify whether a cyclone poses a significant threat to these vulnerable areas. Our holistic research endeavors to enhance the reliability of cyclone intensity estimation and damage prediction. Ultimately, integrating quantum machine learning and convolutional neural networks in cyclone analysis promises to contribute significantly to more effective disaster preparedness and response measures, safeguarding lives and property in cyclone-prone regions.
Quantum image processing represents a transformative approach to visual data analysis, leveraging the principles of quantum computing to overcome classical limitations. This work explores two prominent quantum image encoding methods: FRQI (Flexible Representation of Quantum Images) and NEQR (Novel Enhanced Quantum Representation). FRQI excels in qubit efficiency, making it suitable for hardware implementation, while NEQR offers superior precision in pixel intensity representation, ideal for complex image processing tasks. We detail the implementation of these algorithms, including preprocessing, quantum circuit design, and simulation, using platforms like Qiskit. The study highlights the potential of quantum image processing in fields such as medicine, industry, and environmental monitoring, while addressing challenges like qubit limitations and noise sensitivity. This research contributes to advancing quantum computing applications, paving the way for innovative and sustainable technological solutions.
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.
In the era of big data and artificial intelligence, the increasing volume of data and the demand to solve more and more complex computational challenges are two driving forces for improving the efficiency of data storage, processing, and analysis. Quantum image processing is an interdisciplinary field between quantum information science and image processing, which has the potential to alleviate some of these challenges by leveraging the power of quantum computing. In this work, we compare and examine the compression properties of four different quantum image representations (QImRs): namely, tensor network representation (TNR), flexible representation of quantum image (FRQI), novel enhanced quantum representation (NEQR), and quantum probability image encoding (QPIE). Our simulations show that FRQI and QPIE perform a higher compression of image information than TNR and NEQR. Furthermore, we investigate the trade-off between accuracy and memory in binary classification problems, evaluating the performance of quantum kernels based on QImRs compared to the classical linear kernel. Our results indicate that quantum kernels provide comparable classification average accuracy but require exponentially fewer resources for image storage.
Introduction Accurate prediction of hurricane tracks, intensity, and rapid intensification remains a major challenge in meteorological forecasting. This study investigates whether a hybrid quantum-classical approach, called the Quantum-Enhanced Neural Network (QENN), can improve tropical cyclone prediction. Methods QENN combines atmospheric features from multiple sources with a four-layer, 50-qubit variational circuit, grouped Pauli measurements, and a classical temporal prediction model. The evaluation included 847 Atlantic tropical cyclones recorded between 1980 and 2024. Separate hurricane-disjoint partitions were used for model development, validation, testing, and the 2024 holdout evaluation. Model reliability was further examined through ten random-seed runs, storm-level bootstrap confidence intervals, calibration analysis, comparisons with recent forecasting models, and a width-matched classical ablation study. Results On the locked 2016–2023 test set, QENN achieved a 120-h track mean absolute error of 83.7 km (95% confidence interval [CI]: 78.9–88.6 km), compared with 312.0 km (95% CI: 299.6–325.1 km) for the operational classical ensemble, representing a 73.2% reduction in relative error. For rapid-intensification prediction, QENN achieved a balanced accuracy of 94.7% (95% CI: 91.2%–97.1%), while the classical baseline achieved 67.3%. The median wall-clock time was 50.0 min for QENN and 210.0 min for the classical ensemble, equivalent to a 4.20-fold speed-up and a 76.2% reduction in runtime. In the 2024 holdout evaluation, which included six storms, the mean landfall-position error was 45 km (95% CI: 39–51 km). Discussion The results indicate that quantum-enhanced correlation features may improve forecasting accuracy and computational efficiency within the proposed experimental framework. However, the runtime comparison does not establish a formal quantum computational advantage. Further testing under operational forecasting conditions and validation on quantum hardware are needed to assess the reliability, generalizability, and practical value of the approach.
Image classification is an important task in many practical applications. Recently, there have been various works on classification problems using quantum neural network (QNN). Different data encoders have been investigated, but efficient quantum encoding schemes for high-dimensional data such as images are still an open question. In this study, we investigate popular representation formats of quantum images, namely FRQI and NEQR, as data encoders for quantum neural networks. It is found that the FRQI format not only provides the best classification accuracy compared to NEQR and other popular encoding methods but also is consistent across different circuit depths of QNN. It should be noted that FRQI is notorious for its probabilistic representation in quantum image processing. The finding of this study is interesting because FRQI can play an important role in the field of quantum machine learning.