Quantum computing functions on qubits, different from the classical bits. These qubits follow the properties of quantum physics such as superposition, interference and entanglement. Our aim is to use this quantum technology in the prediction of earthquakes, a natural disaster resulting in a large number of deaths and destruction every year. Earthquakes are one of the most catastrophic natural hazards, and they frequently turn into disasters that cause utter devastation and loss of life. We first implement the prediction on a classical machine learning algorithm and then compare the results with quantum machine learning. Since the processing power of quantum computers is significantly higher than classical computers, it is expected to predict earthquakes accurately and give an early warning to alert the locals residing in that area.
Quantum computing functions on qubits, different from the classical bits. These qubits follow the properties of quantum physics such as superposition, interference and entanglement. Our aim is to use this quantum technology in the prediction of earthquakes, a natural disaster resulting in a large number of deaths and destruction every year. Earthquakes are one of the most catastrophic natural hazards, and they frequently turn into disasters that cause utter devastation and loss of life. We first implement the prediction on a classical machine learning algorithm and then compare the results with quantum machine learning. Since the processing power of quantum computers is significantly higher than classical computers, it is expected to predict earthquakes accurately and give an early warning to alert the locals residing in that area.
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.
Quantum Machine Learning (QML) represents a paradigm shift in the field of artificial intelligence, leveraging the unique properties of quantum mechanics to develop novel algorithms and computational models. This paper explores the potential of QML to address limitations of classical machine learning, particularly in the realm of non-linear learning and prediction. We propose a new quantum machine learning algorithm centered on the application of quantum entanglement and superposition to enhance data representation and model learning. This approach aims to overcome the challenges associated with linear models in handling complex, non-linear data patterns, thereby offering improved predictive accuracy and generalization capabilities. The core of our investigation involves a detailed analysis of the impact of quantum entanglement and superposition on the training process and resulting model outputs. This research will focus on demonstrating the efficacy of this approach through theoretical analysis and, potentially, preliminary experimental validation. The paper concludes with a discussion of the challenges and future directions of this exciting area of research.
Quantum computing has emerged as one of the most promising technologies capable of solving computational problems beyond the capabilities of classical computers. When integrated with Artificial Intelligence (AI), Quantum Machine Learning (QML) offers new possibilities for accelerating data processing, optimizing complex models, and improving predictive performance. Unlike conventional machine learning algorithms that rely solely on classical computation, QML leverages quantum phenomena such as superposition, entanglement, and quantum interference to perform computations more efficiently. This paper presents a comprehensive review of Quantum Machine Learning, discussing its architecture, core algorithms, applications, benefits, limitations, and future research directions. The study highlights the potential of QML in healthcare, cybersecurity, financial analytics, scientific research, and optimization problems. Additionally, the paper examines the challenges associated with quantum hardware, scalability, error correction, and algorithm development. The findings suggest that although Quantum Machine Learning remains in its early stages, it represents a transformative technology that may redefine intelligent computing over the coming decades.
Quantum Machine Learning (QML) is an emerging interdisciplinary field that combines the principles of quantum mechanics and machine learning to develop algorithms that can potentially outperform classical algorithms in certain tasks. QML leverages the unique properties of quantum systems, such as superposition and entanglement, to process information in ways that are not possible with classical computers. This paper provides a comprehensive overview of QML, including its principles, algorithms, and applications. We focus particularly on supervised learning methods, which involve training a quantum model on labeled data to make predictions on new, unseen data. We discuss the potential of QML to revolutionize various domains, such as finance, chemistry, and materials science, and highlight the challenges associated with the development and implementation of QML algorithms, including the need for more advanced quantum hardware and software. This paper aims to provide a clear understanding of the current state of QML research and its potential impact on future computational capabilities.
Quantum Machine Learning (QML) is an emerging interdisciplinary field that combines the principles of quantum mechanics and machine learning to develop algorithms that can potentially outperform classical algorithms in certain tasks. QML leverages the unique properties of quantum systems, such as superposition and entanglement, to process information in ways that are not possible with classical computers. This paper provides a comprehensive overview of QML, including its principles, algorithms, and applications. We focus particularly on supervised learning methods, which involve training a quantum model on labeled data to make predictions on new, unseen data. We discuss the potential of QML to revolutionize various domains, such as finance, chemistry, and materials science, and highlight the challenges associated with the development and implementation of QML algorithms, including the need for more advanced quantum hardware and software. This paper aims to provide a clear understanding of the current state of QML research and its potential impact on future computational capabilities.
Quantum machine learning (QML) has gained attention for its potential to leverage quantum advantages in processing high-dimensional data, capturing complex correlations, and exploring large solution spaces, beyond the limits of classical machine learning. In infrastructure risk, resilience, disruption, and recovery analysis, these capabilities position QML as a promising research direction with not yet realised applicability. This study examines whether QML studies move beyond computational novelty to address the substantive requirements of infrastructure-oriented disaster risk management. Using a systematic literature review of records from Scopus and Web of Science, the search retrieved 784 records before deduplication and retained 33 studies after staged screening and full-text eligibility assessment. Studies were evaluated through an infrastructure risk lens. The evidence base is narrow in sectoral scope, dominated by energy and power applications, and more developed in restoration, dynamic security, cyber-physical protection, operational instability, and disruption-oriented operational analysis than in hazard-explicit infrastructure disaster-risk assessment. Hybrid quantum-classical workflows predominate, while many studies rely on simulated, benchmarked, stylised, or case-specific data with limited validation and uneven reporting of data and preprocessing practices. Although selected studies show potential for restoration, classification, anomaly detection, optimisation, and adaptive coordination, the field remains partly connected to core infrastructure-risk concerns including hazard representation, vulnerability, interdependence, cascading disruption, continuity, and recovery dynamics. QML should therefore be understood not as a mature framework for infrastructure-oriented disaster risk management, but as a promising computational frontier with potential to advance the field through broader sectoral application, stronger empirical validation, multi-hazard integration, and benchmarking against classical methods.