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.
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.
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.
Quantum sensing networks (QSNs) embody a fusion of quantum sensing and quantum communication, achieving both Heisenberg precision and unconditional security through the exploitation of quantum phenomena such as superposition and entanglement. However, the scalability and sensing capability of QSNs in the noisy intermediate-scale quantum (NISQ) era face challenges due to technological constraints, device imperfections, quantum noise, and dynamic sensing scenarios. Recent advancements in integrating quantum non-terrestrial networks (NTNs) and terrestrial networks (TNs) have paved the way for a global-scale quantum internet. Furthermore, leveraging variational quantum sensing (VQS), which capitalizes on hybrid quantum-classical computing, enhances quantum sensing capabilities. VQS tailors quantum sensing probes for dynamic sensing scenarios and noisy environments, significantly advancing application-specific NISQ sensing. We propose an integrated framework by combining VQS for optimal probe preparation and dynamic utility, as well as satellite-ground quantum networking for establishing on-demand entanglement across remote TN nodes. The integrated framework is advantageous over the standalone ground QSNs in terms of network scalability and sensing capability. Simulation results validate the effectiveness of the proposed framework, thereby introducing prospects for scalable and efficient quantum sensing on a global scale.
On the integration of artificial intelligence with quantum sensing technologies for environmental monitoring, these make it much more precise, efficient and real time information end-user.In order to combat climate change markers, insulation effectiveness, and environmental hazard immediate response systems, this research dictates an AI based quantum sensing framework.Quantum augmented machine learning methods, i.e., Quantum Bayesian Networks and Quanvolutional Neural Networks, are used to improve accuracy of measurements from sensors and furthermore sensor calibration.In addition, the proposed system incorporates Q-SCALE for advanced sensor calibration to eliminate noise and improve sensor performance.The Quantum Wireless Sensing framework, in the form of quantum-based sensors, has an advantage in sensing dynamic environmental factors with an increased sensitivity.Some key findings include that AI quantum AI sensors are better than traditional sensors at alerting to abrupt environmental shifts, monitoring pollutants, testing greenhouse conditions, etc.Quantum entanglement was not used, and quantum sensors also achieved secure data transmission of distances beyond 50 kilometers.The proposed system achieves real-time data acquisition using lightweight models optimized for such resource constrained environments and reduces both the energy consumption.This major step in research will lead towards development of more accurate as well as scalable, sustainable environmental monitoring solutions with enhanced data security.As such, recent innovations such as AI coupled with nanomaterials and IoT-instigated quantum surveillance systems indicate the extent of this field's capability.
Quantum communication and quantum sensing, which leverage the unique characteristics of quantum systems, enable information-theoretically secure communication and high-precision measurement of physical quantities. However, they both face numerous challenges on the path to practical application. Artificial intelligence (AI), as a major technological advancement in current scientific landscape, offers powerful data processing and analytical capabilities, providing new ideas and methods for optimizing and enhancing quantum communication and sensing systems. In quantum communication, AI techniques have greatly improved the performance and security of quantum key distribution, quantum memory, and quantum networks through parameter optimization, real-time feedback control, and attack detection. In quantum sensing, quantum sensing technology enables ultra-high sensitivity detection of physical quantities such as time and magnetic fields. The introduction of AI has opened up new avenues for achieving high-precision and high-sensitivity quantum measurements. With AI, sensor performance is optimized, and measurement accuracy is further enhanced through data analysis. This paper also analyzes the current challenges in using AI to empower quantum communication and sensing systems, such as implementing efficient algorithm deployment and system feedback control under limited computational resources, and addressing complex task environments, dynamically changing scenarios, and multi-task coordination requirements. Finally, this paper discusses and envisions future development prospects in this field.
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.
Considering that there are many NP-hard problems in remote sensing (e.g., Craig simplex computation in hyperspectral un-mixing), it is natural to introduce quantum computing into space remote sensing. However, current quantum computers have some practical limitations, requiring complementary techniques to support quantum computing. Specifically, we are introducing artificial intelligence (AI) to solve the quantum collapse effect and the phenomenon of insufficient quantum bits (qubits). We thereby propose the hyperspectral quantum deep network (HyperQUEEN) for satellite remote sensing. HyperQUEEN is the first quantum technology that successfully outputs a complete hyperspectral image, given the very limited qubit resources. Existing quantum image processing methods can only achieve classification-level tasks or simple geometry transforms, and are far from being applicable to advanced satellite missions like restoration of damaged hyperspectral images, which HyperQUEEN has successfully achieved for the first time. Remarkable computational efficiency and restoration performances achieved by the radically new quantum AI system—HyperQUEEN—will be reported.