The ongoing use of satellites is to get information about the earth's features by sensing the radiation reflected/emitted by various objects on the earth’s surface. Since the launch of the first satellite in the year 1957 for defence purposes only, satellites have now become an integral part of human life and play an important role in different sectors including communication, navigation, monitoring weather conditions, etc. These satellites are placed in the earth’s orbit continuously imaging the earth’s surface and generating data of huge sizes. The analysis of this large size data with the help of current generation classical computers is a time-consuming process resulting in a delay in observing the real-time phenomena happening on the earth's surface. The high processing capabilities of quantum computing have emerged as a promising solution for quick analysis of data received from the satellite which may help in monitoring hazards in real time. The quantum computer further will help improve the weather forecasting models and improve the capabilities of artificial neural networks to get useful information from large size data. Here, we will review the usage of quantum computation in existing satellite data processing, its applications in weather forecasting, and real-time monitoring of natural hazard situations through satellites.
The ongoing use of satellites is to get information about the earth's features by sensing the radiation reflected/emitted by various objects on the earth’s surface. Since the launch of the first satellite in the year 1957 for defence purposes only, satellites have now become an integral part of human life and play an important role in different sectors including communication, navigation, monitoring weather conditions, etc. These satellites are placed in the earth’s orbit continuously imaging the earth’s surface and generating data of huge sizes. The analysis of this large size data with the help of current generation classical computers is a time-consuming process resulting in a delay in observing the real-time phenomena happening on the earth's surface. The high processing capabilities of quantum computing have emerged as a promising solution for quick analysis of data received from the satellite which may help in monitoring hazards in real time. The quantum computer further will help improve the weather forecasting models and improve the capabilities of artificial neural networks to get useful information from large size data. Here, we will review the usage of quantum computation in existing satellite data processing, its applications in weather forecasting, and real-time monitoring of natural hazard situations through satellites.
Quantum computing (QC) promises to revolutionize data-intensive fields due to its potential for exponential speedups and novel computational paradigms. Satellite image processing (also called Earth Observation, EO) is one such domain, where large volumes of high-dimensional data (e.g., multispectral or hyperspectral images) challenge classical methods in terms of computation, memory, and latency. In this review, we survey the state-of-the-art at the intersection of quantum computing and satellite image processing. We first outline quantum image representations, key quantum algorithms proposed for image processing (e.g., edge detection, segmentation, classification), and quantum machine learning (QML) models. We then examine recent studies that target Earth observation data, including resource estimation and hybrid quantum-classical architectures. Next, we delineate the major challenges (hardware, encoding, noise, scalability) and propose potential solutions, focusing on suitable quantum algorithms and hybrid strategies. Finally, we discuss future directions and research opportunities.
Climate prediction plays a crucial role in understanding and mitigating the impacts of climate change. Traditional climate models, though powerful, often face challenges in handling the vast, high-dimensional satellite imagery data required for accurate forecasting. Meanwhile, quantum computing offers new computational paradigms capable of addressing complex problems intractable for classical systems. In this research, we propose an innovative framework for an image-based climate prediction model leveraging quantum computing technology. By integrating quantum machine learning algorithms with satellite Earth observation data, the model aims to extract critical climatic patterns such as temperature anomalies, cloud movements, and vegetation dynamics with enhanced precision and computational efficiency. The study addresses existing limitations by developing specialized quantum algorithms for image processing, exploring quantum-classical hybrid architectures, and optimizing the system for real-world scalability using current and near-term quantum devices. Preliminary results suggest that quantum approaches have the potential to outperform traditional techniques in processing speed and predictive accuracy, particularly for high-resolution, time-series satellite imagery. This work represents a significant step toward practical, quantum-enhanced climate modeling, bridging the gap between quantum computing advancements and urgent global climate forecasting needs.
Earth observation satellites (EOS) play a crucial role in collecting data for applications like weather forecasting and disaster management. Optimizing EOS missions to capture high-priority targets within constraints such as storage, energy, and weather is challenging for traditional computing methods. Quantum computing shows promise in enhancing efficiency by exploring vast solution spaces for optimal schedules, even with complex constraints. This paper demonstrates the potential of a quantum algorithm to improve real-time EOS mission planning, maximizing high-priority target acquisition under resource limitations. Compared to classical algorithms like simulated annealing and Gurobi optimizer, the proposed quantum algorithm outperformed in selecting high-priority targets by 23.46%, executed faster than Gurobi by 39.09%, and successfully satisfied all constraints for all data.
Acceleration of image segmentation using deep learning methods on satellite imagery has become ubiquitous in various applications areas such as land cover classification, disaster monitoring and vegetation detection. However, the increase in satellite image resolution and large data volume required for remote sensing applications has resulted in a substantial increase in computational resource usage and demand for real-time processing. This paper investigates quantum computing as a novel approach to meet these computational demands, exploiting its parallel processing strengths. We evaluate hybrid quantum models (COQCNN, MQCNN, FQCNN) against classical CNN and U-Net architectures in remote sensing classification. Although COQCNN and MQCNN underperformed, FQCNN reached 53.26% accuracy, outperforming the classical CNN by 8%. Despite quicker convergence, quantum models struggle with complex feature segmentation, a task where U-Net excels. This study highlights quantum convolutions as a potential path to enhance convergence while addressing challenges like noise from multiple quantum channels affecting accuracy.
Satellite imagery plays a crucial role in integrated satellite-ground remote sensing (SGRS), particularly in applications such as disaster management and military intelligence, where real-time monitoring and forecasting are essential for effective decision-making. However, narrow artificial intelligence (AI) models often face challenges in processing large-scale high-dimensional data efficiently while maintaining the required accuracy and speed, limiting their effectiveness in time-sensitive scenarios. To address these challenges, we explore the integration of satellite remote sensing with large AI, quantum computing, and quantum communication technologies, focusing on enhancing computational efficiency and data security in integrated SGRS systems. Specifically, we put forth an integrated quantum SGRS framework, which combines quantum fusion intelligence (QFI) with quantum anonymous communication (QAC). By integrating quantum and large AI, the QFI models enhance the efficiency, accuracy, and security of satellite imagery analysis while ensuring that the extracted information is transmitted to ground stations in a privacy-preserving manner using QAC. This approach is particularly effective in time- and privacy-sensitive scenarios. To demonstrate the effectiveness of QFI computing, we present case studies in disaster detection and environmental monitoring. This research highlights the transformative potential of quantum-large AI integration in SGRS and its implications for nonterrestrial-terrestrial quantum networks.
Classical computers have been present for a long time and they have played a significant role in scientific advancements. Quantum computing has shown good results in solving complex problems. Quantum computers use phenomena of quantum superposition and quantum entanglement to form states that scale exponentially with the number of qubits or quantum bits [1]. Classical computers use individual bits, 0 and 1 to store information as binary data & quantum computers use the probability of state before it is measured [2]. Therefore, it gives them a potential to process exponentially more data as compared to classical computers. Unlike classical computers that use binary bits, quantum computers use qubits that are produced by quantum state of object to perform operations. Since, these qubits are quantum, they follow phenomena like quantum superposition and entanglement. Superposition is ability of a quantum system to be in multiple states at the same time. Entanglement is the strong correlation among quantum particles. These phenomena help quantum computers work with 0, 1 and superposition of 0 and 1, giving it advantage of doing complex calculations that classical systems cannot do or take a significant amount of time to get desired results [3]. Quantum computing is used because of its potential for changing time and space complexity of many algorithms we have been using as a solution to linear system of equations [4]. Quantum simulation is one of the most prominent areas of quantum computers, it has the potential to solve the complexities of molecular and chemical interactions which can lead to the discovery of new medicines and materials. Various applications of quantum computing in several significant areas of computer science, such as cryptography, machine learning, deep learning and quantum simulations. They also use various real-life scenarios such as risk analysis, logistics and satellite communication [6].