Natural disasters like tsunamis pose significant threats to coastal communities worldwide, necessitating advanced prediction systems for effective early warning. This study introduces the Quantum-Enhanced Deep Learning for Tsunami Prediction (QEDLTP) architecture, a novel approach combining quantum computing principles with deep learning techniques. Utilizing an extensive dataset spanning 1995-2023, comprising 18,742 seismic events and bathymetric measurements, our framework implements a dual-stream processing architecture with Adaptive Quantum Resonance Circuits (AQRC). The model employs an innovative Quantum Feature Selection (QFS) mechanism that dynamically adjusts quantum entanglement patterns based on input characteristics. The Proposed approach processes seismic waveforms from 2,000 global stations and high-resolution bathymetric data simultaneously, achieving 98.7% prediction accuracy with response times under 3 seconds. The architecture demonstrates superior performance in complex geological settings, reducing false positives by 76% compared to traditional methods. The QEDLTP framework’s quantum-enhanced processing enables 78% feature dimensionality reduction while maintaining 96.5% information retention, significantly improving computational efficiency. Testing across 250 historical tsunami events validates the model’s robustness and reliability. This research establishes new benchmarks in tsunami prediction systems and opens avenues for quantum computing applications in geophysical analysis and natural disaster prediction.
Natural disasters like tsunamis pose significant threats to coastal communities worldwide, necessitating advanced prediction systems for effective early warning. This study introduces the Quantum-Enhanced Deep Learning for Tsunami Prediction (QEDLTP) architecture, a novel approach combining quantum computing principles with deep learning techniques. Utilizing an extensive dataset spanning 1995-2023, comprising 18,742 seismic events and bathymetric measurements, our framework implements a dual-stream processing architecture with Adaptive Quantum Resonance Circuits (AQRC). The model employs an innovative Quantum Feature Selection (QFS) mechanism that dynamically adjusts quantum entanglement patterns based on input characteristics. The Proposed approach processes seismic waveforms from 2,000 global stations and high-resolution bathymetric data simultaneously, achieving 98.7% prediction accuracy with response times under 3 seconds. The architecture demonstrates superior performance in complex geological settings, reducing false positives by 76% compared to traditional methods. The QEDLTP framework’s quantum-enhanced processing enables 78% feature dimensionality reduction while maintaining 96.5% information retention, significantly improving computational efficiency. Testing across 250 historical tsunami events validates the model’s robustness and reliability. This research establishes new benchmarks in tsunami prediction systems and opens avenues for quantum computing applications in geophysical analysis and natural disaster prediction.
Abstract Tsunami is one of the deadliest natural disasters which can occur, leading to great loss of life and property. This study focuses on predicting tsunamis, using earthquake dataset from the year 1995 to 2023. The research introduces the Hybrid Quantum Neural Network (HQNN), an innovative model that combines Neural Network (NN) architecture with Parameterized Quantum Circuits (PmQC) to tackle complex machine learning (ML) problems where deep learning (DL) models struggle, aiming for higher accuracy in prediction while maintaining a compact model size. The hybrid model’s performance is compared with the classical model counterpart to investigate the quantum circuit’s effectivity as a layer in a DL model. The model has been implemented using 2-6 features through Principle Component Analysis (PCA) method. HQNN’s quantum circuit is a combination of Pennylane’s embedding (Angle Embedding (AE) and Instantaneous Quantum Polynomial (IQP) Embedding) and layer circuits (Basic Entangler Layers (BEL), Random Layers (RL), and Strongly Entangling Layers (SEL)), along with the classical layers. Results show that the proposed model achieved high performance, with a maximum accuracy up to 96.03% using 4 features with the combination of AE and SEL, superior to the DL model. Future research could explore the scalability and diverse applications of HQNN, as well as its potential to address practical ML challenges.
Abstract This paper presents a groundbreaking application of Quantum Machine Learning (QML) in geophysics, specifically targeting seismic fault detection. By integrating quantum computing with machine learning, the study addresses critical limitations of traditional fault detection methods, such as the detection of subtle features, processing efficiency, and scalability. The primary objective is to enhance fault delineation accuracy and provide a robust framework suitable for large-scale seismic surveys and real-time applications. The methodology involves encoding seismic data into quantum states to efficiently handle high-dimensional features. A hybrid quantum-classical model is developed, utilizing quantum-enhanced algorithms for fault detection. This model incorporates Quantum Long Short-Term Memory (QLSTM) networks, which leverage quantum parallelism and interference to enhance the processing capabilities of traditional LSTM networks. The QLSTM architecture employs quantum gates and memory cells to maintain and regulate the flow of quantum information, enabling more efficient and accurate processing of sequential data. The workflow encompasses quantum-based feature extraction, model training with seismic datasets, and validation using both synthetic and real-world examples. Comparative studies with traditional machine learning methods assess performance metrics, including accuracy, noise suppression, and computational efficiency. Quantum hardware simulators ensure the method's feasibility for practical applications. Results demonstrate that QML, powered by QLSTM, significantly outperforms conventional methods in seismic fault detection, achieving higher accuracy, better resolution of subtle geological features, and reduced noise sensitivity. Comprehensive testing on synthetic and field datasets reveals QML's ability to capture complex fault patterns, particularly in geologically challenging settings. Additionally, QML's computational efficiency enables rapid analysis of large datasets, facilitating timely decision-making. In conclusion, QML emerges as a superior approach for seismic fault detection, addressing longstanding challenges and offering novel opportunities for real-time seismic interpretation. By leveraging quantum computing, QML provides unprecedented insights into subsurface structures, leading to more accurate predictions and informed decisions in exploration and production operations. This paper introduces a transformative approach to seismic fault detection, contributing significantly to existing literature and offering actionable insights for the petroleum industry, particularly in subsurface imaging and reservoir characterization.
Seismic prediction remains challenging due to the highly nonlinear and chaotic dynamics of earthquake signals. While classical deep learning models such as LSTMs and CNNs capture local temporal features, and quantum models offer richer state representations, their integration with chaos-driven mechanisms is underexplored. We introduce QuChaTeR, a hybrid architecture that combines wavelet-based preprocessing, chaotic maps, and variational quantum circuits with recurrent structures to enhance temporal feature extraction. Implemented in PyTorch and PennyLane, QuChaTeR is benchmarked against classical (LSTM, GRU, RNN, 1D-CNN, Reservoir Computing) and quantum-inspired (Quantum LSTM) baselines. On real-world seismic datasets, QuChaTeR consistently converges faster and achieves superior performance across multiple evaluation criteria. Despite promising results, scalability and quantum hardware limitations remain challenges. Overall, this work demonstrates how quantum-chaotic hybridization provides a practical pathway toward more accurate and robust earthquake prediction.
Cyberattacks are increasing in sophistication. As a result, traditional cybersecurity systems based on classical computing and conventional deep-learning techniques are increasingly exhibiting limitations and vulnerabilities. Although deep-learning models offer substantial advantages over traditional methods for automated intrusion detection through large-scale security data analysis and pattern recognition, they suffer from notable constraints related to high-dimensional feature spaces, scalability, and stringent real-time inference requirements. This work proposes a quantum-enhanced deep learning (QEDL) framework for detecting and mitigating real-time cyberattack patterns. Quantum computing technologies—specifically quantum feature selection and quantum data encoding—are integrated into a classical–hybrid quantum deep-learning architecture. The quantum approximate optimization algorithm (QAOA) is employed for quantum feature selection to enable efficient identification of discriminative features from high-dimensional cybersecurity datasets. In addition, quantum data encoding exploits superposition and entanglement to process multiple complex inputs simultaneously. The resulting quantum feature representations are processed using parameterized quantum circuits (PQCs), followed by classical deep-learning layers for accurate threat classification. The proposed QEDL framework is evaluated using the CIC–IDS2017 dataset, and its performance is compared against classical deep-learning models and pure quantum neural networks. Experimental results demonstrate that, within the proposed hybrid quantum–classical framework, the QAOA-based feature selection strategy achieved higher classification performance than the evaluated classical feature selection methods under the reported experimental settings. In addition, the proposed framework exhibited improved generalization to previously unseen attacks. These findings suggest that the proposed hybrid quantum–classical framework is a promising approach for cyberattack detection under the experimental conditions considered in this study. The experiments in this study were simulated using IBM Aer backend, without physical quantum hardware. The results obtained in this study serve as a simulation-based proof of concept.
Quantum-Enhanced Machine Learning (QEML) is a new computational paradigm that introduces the concept of quantum mechanics with the latest artificial intelligence algorithms to enhance the performance of the diagnostic process in the field of medical imaging. The early identification of diseases is crucial in mortality reduction and improving patient outcome, especially in capacities dealing with such diseases as cancer, neurological diseases, heart diseases, and infections of the lungs. Although conventional deep learning architectures, in particular, Convolutional Neural Networks (CNNs) have been highly successful in processing complex medical images such as MRI scans, CT images, X-rays, and histopathological data, they can be computationally expensive, need large data sets, and suffer in optimization of high dimensional feature spaces. The limitations that QEML tries to overcome are based on the quantum mechanical principles of superposition, entanglement and interference to facilitate better data representation and processing. The suggested structure is based on the classical deep learning architectures and variational quantum circuits together, to create a hybrid quantum-classical system, which improves features extraction and classification levels. Under this method, the classical image characteristics are coded into quantum states and processed in high-dimensional Hilbert spaces, which can enhance the separability of subtle disease patterns, which would have otherwise been challenging to obtain with conventional techniques. The objective of this hybrid system is to enhance the critical diagnostic measures like sensitivity and specificity without affecting the interpretability of such measures to be used in the clinical context. The proposed framework is also assessed in terms of its feasibility, its computational performance and scalability to the capabilities of present-day Noisy Intermediate-Scale Quantum (NISQ) devices. The model effectiveness is evaluated using objective evaluation metrics, which, in a way, guarantee the feasibility of the model application within a real-life healthcare context. On balance, quantum computing and medical image analysis integration is an exciting field of work of the future intelligent healthcare systems and has a potential of providing more efficient, accurate, and scalable diagnostic solutions.
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.