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International Journal of Cloud Computing

Integrating Edge Computing and Quantum Computing for Efficient Data Processing in Disaster Recovery Systems

Hitendra Ashok Chavan, Prashant Nitnaware

Published Dec 7, 2025• 0 citations
Publisher
DOI
10.1504/ijcc.2026.10075036

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Since quantum computing is based on ideas of superposition and entanglement, it will open new horizons in the field of disaster recovery and relief. Based on principles like superposition and entanglement, quantum computing holds immense and revolutionary potential in disaster relief operations. This is because, in the massive volumes of data produced during any kind of disaster, combined with the high level of complexity, response times and accuracy are often out of reach using traditional methods of computing. Quantum computing plays a revolutionary role in providing optimizations to the several stages of disaster management: prediction, readiness, response, and recovery. Quantum computers can be used to generate complex simulations using quantum parallelism and predict extreme weather conditions or several other disasters that can prove to be destructive to life and property. This can also be used to implement efficient evacuation in a timely manner. Quantum algorithms can also make resource allocation more efficient and ensure that essential supplies reach affected regions faster. Real-time processing and analysis of huge datasets enable good coordination among response teams and support dynamic decision-making. This article gives a very broad overview of the applications of quantum computing in disaster management protocols, providing insights into how quantum-enhanced algorithms can be used for generating simulations and for real-time analysis. These results are focused on developing efficient disaster management techniques that can better serve communities in the face of the increasing occurrence of natural and man-made disasters.
Disaster response and recovery activities often suffer from challenges such as slow decision-making, mismanagement of resources, and inadequate coordination of robots in dynamic settings. In response to the aforementioned problems, this research presents a new paradigm called Quantum Cooperative Robotics Framework (QCRF) that integrates quantum computing in functional cooperative robotic systems for disaster conditions. QCRF combines quantum-assisted resource management, quantum swarm factor, quantum secure communication, quantum reinforcement learning, and blockchain and advanced decision awareness. Quantum optimization can help with real-time decisions of where to allocate resources so that robots can quickly be dispatched to high-risk areas. Swarm intelligence inspired by quantum computing helps to solve problems associated with robotic tasks’ flexible distribution and path choices in challenging environments. QKD plays a crucial role in assuring communication between robots in dangerous surroundings, since it provides secure and authenticated keys. Also, quantum reinforcement learning enables robots to respond quickly to rather extensive and sudden changes in the environment, for instance, the collapse of a buildings or changing climate. The use of the blockchain also gives openness and traceability in the decision-making process, encouraging the necessity of providing explanations to the parties concerned. The paper also presents real-time solutions of the proposed framework in earthquake response, flood management, and wildfire control that prove the flexibility of the framework. Remote performance evaluations point out increased decision-making speeds, task productivity, and user data security as compared to traditional systems. Simultaneously, the metrics indicated in this study not only solve existing issues in disaster coordination, but also lay the groundwork for the further growth of quantum cooperative robotics, which can be used in smart city development, agriculture, and space exploration. Catering for the integration of two disruptive technologies, i.e., quantum computing and robotic intelligence, this framework poses a revolutionary solution for minimizing the adverse effects of natural disasters, as well as increasing the general resiliency of the world.
The rapid growth of data generated by Internet of Things (IoT) devices necessitates the development of advanced computational frameworks that can efficiently handle real-time data processing. Traditional cloud and edge computing systems, while effective, face significant challenges, including high latency, limited computational resources, and data transfer bottlenecks. To address these issues, this study introduces a hybrid approach that integrates quantum computing into the edge-cloud continuum. The proposed method utilizes Quantum- Neural Networks (QNNs) in conjunction with local edge processing, leveraging quantum autoencoders for feature compression, and incorporating fully decentralized edge computing with quantum processors to enhance data processing capabilities. Additionally, a hybrid model combining QNNs with classical machine learning techniques is implemented at the edge for improved classification accuracy. The proposed system's performance is evaluated through extensive experiments across multiple datasets, comparing the results of quantum-based models with traditional classical and hybrid approaches. The results show a significant improvement in classification accuracy, reduced feature compression time, and enhanced processing efficiency, particularly in resource-constrained environments. The results underscore the potential of quantum machine learning in real-time, large-scale applications, marking an important step toward the practical integration of quantum computing in distributed computing systems.