Accurately forecasting how people move during natural disasters is essential for helping communities stay safe and recover quickly. Information from mobile phones, GPS, and social media gives a real time view of where people are and how they’re moving. This helps emergency teams make more time effective and accurate decisions. But traditional computer models can not handle this kind of complex and fast-changing data, especially when time is critical. This study investigates the use of quantum machine learning (QML), with a particular emphasis on quantum neural networks and hybrid quantumclassical models, to forecast mobility patterns during disasters. A structured dataset containing individual movement information within a 15 by 20 kilometer region was used to train and evaluate multiple forecasting models. The results show that the hybrid quantum-classical model achieved the highest forecasting accuracy, with a coefficient of determination ($R^{2}$) of 0.91 and a mean absolute error of approximately 1143.33 individuals. This model also maintained a good balance between predictive performance and complexity of the model, as shown in its AIC and BIC scores. In comparison, the classical multilayer perceptron performed competitive results with an $\mathbf{R}^{2}$ of $\mathbf{0. 8 9}$, while the generalized quantum model yielded an $\mathbf{R}^{2}$ of $\mathbf{0. 8 1}$. The LSTM model underperformed and it gave unstable predictions and had much higher errors. These findings highlight the potential of hybrid quantum models particularly QNN based architectures to enhance disaster preparedness by delivering accurate and scalable mobility forecasts in critical situations where early decisions are essential.
Accurately forecasting how people move during natural disasters is essential for helping communities stay safe and recover quickly. Information from mobile phones, GPS, and social media gives a real time view of where people are and how they’re moving. This helps emergency teams make more time effective and accurate decisions. But traditional computer models can not handle this kind of complex and fast-changing data, especially when time is critical. This study investigates the use of quantum machine learning (QML), with a particular emphasis on quantum neural networks and hybrid quantumclassical models, to forecast mobility patterns during disasters. A structured dataset containing individual movement information within a 15 by 20 kilometer region was used to train and evaluate multiple forecasting models. The results show that the hybrid quantum-classical model achieved the highest forecasting accuracy, with a coefficient of determination (R2) of 0.91 and a mean absolute error of approximately 1143.33 individuals. This model also maintained a good balance between predictive performance and complexity of the model, as shown in its AIC and BIC scores. In comparison, the classical multilayer perceptron performed competitive results with an R2 of 0.89, while the generalized quantum model yielded an R2 of 0.81. The LSTM model underperformed and it gave unstable predictions and had much higher errors. These findings highlight the potential of hybrid quantum models particularly QNN based architectures to enhance disaster preparedness by delivering accurate and scalable mobility forecasts in critical situations where early decisions are essential.
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.
This study investigates the potential of quantum machine learning to enhance the accuracy of typhoon forecasting, with a particular emphasis on its implications for photovoltaic (PV) and wind turbine systems in the context of extreme weather preparedness. A novel approach is proposed, integrating a quantum layer into a multi-model Long Short-Term Memory (LSTM) network architecture. Implemented using CUDA-Q, the quantum layer leverages quantum phenomena to process typhoon-related data more effectively. The proposed quantum-enhanced LSTM model is evaluated on a large dataset of typhoon observations. Results demonstrate that the model achieves promising accuracy in both typhoon track forecasting—yielding mean absolute errors (MAE) of 0.0188 and 0.0307 for latitude and longitude, respectively—and intensity forecasting, with MAEs of 0.0233 for wind speed, 0.0449 for wind direction, and 0.0022 for the 50-knot wind radius. This research contributes to the growing field of quantum machine learning and its practical applications, paving the way for future advancements in typhoon prediction and disaster resilience, particularly in optimizing the operation and robustness of PV and wind turbine systems in typhoon-prone regions.
In numerous industries, weather forecasting is essential for making informed decisions and mitigating the effects of extreme weather events. The complexity and chaos of weather systems, however, place restrictions on standard procedures, leading to errors and significant threats. We suggest the Quantum Improved Weather Forecast framework, which combines quantum machine learning methods with conventional methodologies, to solve these issues. By using quantum algorithms including quantum support vector machines, quantum neural networks, and quantum clustering, the QWF framework seeks to improve accuracy. Despite a few drawbacks, the QWF framework provides a method to revolutionize weather forecasting by enhancing prediction accuracy and facilitating improved catastrophe preparedness. It may prevent fatalities, safeguard critical infrastructure, and promote sustainable growth.
Accurate traffic forecasting plays a crucial role in modern Intelligent Transportation Systems (ITS), as it enables real-time traffic flow management, reduces congestion, and improves the overall efficiency of urban transportation networks. With the rise of Quantum Machine Learning (QML), it has emerged a new paradigm possessing the potential to enhance predictive capabilities beyond what classical machine learning models can achieve. In the present work we pursue a heuristic approach to explore the potential of QML, and focus on a specific transport issue. In particular, as a case study we investigate a traffic forecast task for a major urban area in Athens (Greece), for which we possess high-resolution data. In this endeavor we explore the application of Quantum Neural Networks (QNN), and, notably, we present the first application of quantum data re-uploading in the context of transport forecasting. This technique allows quantum models to better capture complex patterns, such as traffic dynamics, by repeatedly encoding classical data into a quantum state. Aside from providing a prediction model, we spend considerable effort in comparing the performance of our hybrid quantum-classical neural networks with classical deep learning approaches. Our results show that hybrid models achieve competitive accuracy with state-of-the-art classical methods, especially when the number of qubits and re-uploading blocks is increased. While the classical models demonstrate lower computational demands, we provide evidence that increasing the complexity of the quantum model improves predictive accuracy. These findings indicate that QML techniques, and specifically the data re-uploading approach, hold promise for advancing traffic forecasting models and could be instrumental in addressing challenges inherent in ITS environments.
Accurate traffic forecasting plays a crucial role in modern Intelligent Transportation Systems (ITS), as it enables real-time traffic flow management, reduces congestion, and improves the overall efficiency of urban transportation networks. With the rise of Quantum Machine Learning (QML), it has emerged a new paradigm possessing the potential to enhance predictive capabilities beyond what classical machine learning models can achieve. In the present work we pursue a heuristic approach to explore the potential of QML, and focus on a specific transport issue. In particular, as a case study we investigate a traffic forecast task for a major urban area in Athens (Greece), for which we possess high-resolution data. In this endeavor we explore the application of Quantum Neural Networks (QNN), and, notably, we present the first application of quantum data re-uploading in the context of transport forecasting. This technique allows quantum models to better capture complex patterns, such as traffic dynamics, by repeatedly encoding classical data into a quantum state. Aside from providing a prediction model, we spend considerable effort in comparing the performance of our hybrid quantum-classical neural networks with classical deep learning approaches. We observe that, in fully connected network settings, hybrid quantum-classical models consistently underperform, with median scores approximately 10% worse than their purely classical counterparts across different configurations. In contrast, recursive architectures with data re-uploading show the opposite trend: hybrid models achieved up to 5% better median scores under comparable complexity settings. Additionally, these hybrid models converged in fewer training epochs, indicating improved training efficiency. Our results show that hybrid models achieve competitive accuracy with state-of-the-art classical methods, especially when the number of qubits and re-uploading blocks is increased. While the classical models demonstrate lower computational demands, we provide evidence that increasing the complexity of the quantum model improves predictive accuracy. These findings indicate that QML techniques, and specifically the data re-uploading approach, hold promise for advancing traffic forecasting models and could be instrumental in addressing challenges inherent in ITS environments.
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.