Managing the response to natural disasters effectively can considerably mitigate their devastating impact. This work explores the potential of using supervised hybrid quantum machine learning to optimize emergency evacuation plans for cars during natural disasters. The study focuses on earthquake emergencies and models the problem as a dynamic computational graph where an earthquake damages an area of a city. The residents seek to evacuate the city by reaching the exit points where traffic congestion occurs. The situation is modeled as a shortest-path problem on an uncertain and dynamically evolving map. We propose a novel hybrid supervised learning approach and test it on hypothetical situations on a concrete city graph. This approach uses a novel quantum feature-wise linear modulation (FiLM) neural network parallel to a classical FiLM network to imitate Dijkstra's node-wise shortest path algorithm on a deterministic dynamic graph. Adding the quantum neural network in parallel increases the overall model's expressivity by splitting the dataset's harmonic and non-harmonic features between the quantum and classical components. The hybrid supervised learning agent is trained on a dataset of Dijkstra's shortest paths and can successfully learn the navigation task. The hybrid quantum network improves over the purely classical supervised learning approach by 7% in accuracy. We show that the quantum part has a significant contribution of 45.(3)% to the prediction and that the network could be executed on an ion-based quantum computer. The results demonstrate the potential of supervised hybrid quantum machine learning in improving emergency evacuation planning during natural disasters.
Managing the response to natural disasters effectively can considerably mitigate their devastating impact. This work explores the potential of using supervised hybrid quantum machine learning to optimize emergency evacuation plans for cars during natural disasters. The study focuses on earthquake emergencies and models the problem as a dynamic computational graph where an earthquake damages an area of a city. The residents seek to evacuate the city by reaching the exit points where traffic congestion occurs. The situation is modeled as a shortest-path problem on an uncertain and dynamically evolving map. We propose a novel hybrid supervised learning approach and test it on hypothetical situations on a concrete city graph. This approach uses a novel quantum feature-wise linear modulation (FiLM) neural network parallel to a classical FiLM network to imitate Dijkstra's node-wise shortest path algorithm on a deterministic dynamic graph. Adding the quantum neural network in parallel increases the overall model's expressivity by splitting the dataset's harmonic and non-harmonic features between the quantum and classical components. The hybrid supervised learning agent is trained on a dataset of Dijkstra's shortest paths and can successfully learn the navigation task. The hybrid quantum network improves over the purely classical supervised learning approach by 7% in accuracy. We show that the quantum part has a significant contribution of 45.(3)% to the prediction and that the network could be executed on an ion-based quantum computer. The results demonstrate the potential of supervised hybrid quantum machine learning in improving emergency evacuation planning during natural disasters.
The pathfinding problem in a graph has been solved using several classical algorithms, notably Dijkstra’s and A* algorithms. However, most classical algorithms are most effective on static graphs. They either cannot be adapted to dynamic graphs or become computationally expensive. The challenge of processing dynamic graphs, which demands significant computational resources, can be addressed using Classical and Quantum Machine Learning methods. For this review, we consider a dynamically changing graph representing a disaster-stricken city. Our problem, termed the Disaster Escape Routing problem, aims to find the optimal path within this dynamic graph. We review and analyze an existing hybrid quantum-classical machine learning model alongside classical machine learning models specifically for this problem. We also explore Variational Quantum Circuits and Encoding Methods. Our study suggests that hybrid quantum-classical machine learning, Graph Neural Networks, and Temporal Graph Networks offer high performance in terms of path prediction and accuracy in finding the optimal path. This review also identifies Kolmogorov Arnold Networks as a promising approach to solving escape routing problems. Additionally, an integrated approach combining strengths of all the models has been hypothesized to enhance emergency escape route planning.
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.
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.
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 has recently emerged as a promising paradigm that leverages the expressive power of quantum circuits to address complex learning tasks. In this work, we investigate the applicability of hybrid quantum-classical neural networks to sentiment analysis, a central problem in natural language processing. We focus on a dataset of tweets related to COVID-19, where the textual content is vectorized using TF-IDF and fed into both classical feedforward networks and hybrid architectures incorporating parameterized quantum circuits. Our results show that hybrid models can achieve accuracy comparable to the classical baseline, while exhibiting distinct learning dynamics, especially in terms of validation loss and accuracy, that suggest a richer representational capacity. Moreover, when applying transfer learning to an SMS spam classification task, the hybrid models consistently outperform the classical counterpart, achieving an accuracy increase of 15 percentage points (from 66% to 81%) on the spam class, demonstrating enhanced generalization. These findings highlight the feasibility of employing QML for natural language processing and point toward the potential advantages of hybrid models as quantum hardware continues to advance.
Image classification is a fundamental task in deep learning, and recent advances in quantum computing have generated significant interest in quantum neural networks. Traditionally, Convolutional Neural Networks (CNNs) are employed to extract image features, while Multilayer Perceptrons (MLPs) handle decision making. However, parameterized quantum circuits offer the potential to capture complex image features and define sophisticated decision boundaries. In this paper, we present a novel Hybrid Quantum–Classical Neural Network (H-QNN) for image classification, and demonstrate its effectiveness using the MNIST dataset. Our model combines quantum computing with classical supervised learning to enhance classification accuracy and computational efficiency. In this study, we detail the architecture of the H-QNN, emphasizing its capability in feature learning and image classification. Experimental results demonstrate that the proposed H-QNN model outperforms conventional deep learning methods in various training scenarios, showcasing its effectiveness in high-dimensional image classification tasks. Additionally, we explore the broader applicability of hybrid quantum–classical approaches in other domains. Our findings contribute to the growing body of work in quantum machine learning, and underscore the potential of quantum-enhanced models for image recognition and classification.