This paper explores the integration of low-altitude CubeSats for signal telemetry while enhancing them for the acquisition of imaging data, with a focus on lunar surveying and high-resolution image reconstruction. The proposed framework involves enhancing a CubeSat with multimodal sensing capabilities, including radar, lidar, and optical imaging (with a fish-eye lens camera) to capture high-resolution imaging data from long-range signals. To address the computational challenges associated with reconstructing geospatial imagery from such data, this work investigates the use of qutrit-based quantum optimization circuits for image reconstruction. The core contribution is a qutrit-based reconstruction technique designed for both improved reconstruction fidelity and optimization of grayscale and color geospatial images. The approach leverages quantum image representation methods and integrates a specialized onboard compute module informed by existing signal processing methods. By applying qutrit-based processing to raw geospatial data derived from multiple signal types, the framework aims to enhance reconstruction performance in terms of speed and image quality, alongside a qubit-based grid computing architecture for quantum optimization. The proposed methodology is situated within the current related literature, providing foundational support for subsequent experiments aimed at improving lunar surveying and imaging capabilities.
This paper explores the integration of low-altitude CubeSats for signal telemetry while enhancing them for the acquisition of imaging data, with a focus on lunar surveying and high-resolution image reconstruction. The proposed framework involves enhancing a CubeSat with multimodal sensing capabilities, including radar, lidar, and optical imaging (with a fish-eye lens camera) to capture high-resolution imaging data from long-range signals. To address the computational challenges associated with reconstructing geospatial imagery from such data, this work investigates the use of qutrit-based quantum optimization circuits for image reconstruction. The core contribution is a qutrit-based reconstruction technique designed for both improved reconstruction fidelity and optimization of grayscale and color geospatial images. The approach leverages quantum image representation methods and integrates a specialized onboard compute module informed by existing signal processing methods. By applying qutrit-based processing to raw geospatial data derived from multiple signal types, the framework aims to enhance reconstruction performance in terms of speed and image quality, alongside a qubit-based grid computing architecture for quantum optimization. The proposed methodology is situated within the current related literature, providing foundational support for subsequent experiments aimed at improving lunar surveying and imaging capabilities.
Geospatial analysis of satellite imagery has become indispensable for environmental monitoring, disaster management, and urban planning, yet the growing volume of Synthetic Aperture Radar (SAR) and optical Earth-observation data continues to outpace the capacity of conventional processing pipelines. Existing geospatial toolchains remain fragmented across proprietary software, demand substantial preprocessing expertise, and rely on purely classical learning algorithms whose clustering and segmentation performance degrades on high-dimensional, speckle-affected SAR feature spaces. To address these limitations, this paper presents a quantum-enhanced extension of GeoLens, an open-source Python framework for end-to-end geospatial raster processing, and proposes two novel methods: (i) Quantum Cluster-Visualized Computing (QCVC), a q-means-inspired clustering algorithm that employs amplitude encoding and SWAP-test distance estimation to generate interpretable, cluster-visualized land-cover maps from unlabeled SAR features, and (ii) QE-IAS-UNet, a hybrid quantum-enhanced Inception-Attention-Skip U-Net in which a quanvolutional front-end layer built from four-qubit variational circuits enriches multi-scale feature extraction for semantic segmentation. Experiments are designed on SAR and optical products obtained from the ISRO Bhoonidhi portal together with openly available labeled benchmarks, covering five land-cover classes. In the reported evaluation protocol, QCVC improves the mean silhouette coefficient from 0.55 to 0.61 over classical k-means at the optimal cluster number k = 5, while QE-IAS-UNet attains 96.4% overall accuracy and 95.8% F1-score, exceeding U-Net, ResUNet, PSPNet, DeepLabv3, and Attention U-Net baselines by 1.8-8.2 percentage points. The principal contributions are a unified quantum-classical geospatial processing architecture, two reproducible algorithms with explicit mathematical formulations and cutoff criteria, and a comprehensive evaluation methodology spanning accuracy, similarity, and cluster-validity metrics. Future work targets deployment on near-term quantum hardware and federated multi-sensor extension......
Synthetic Aperture Radar (SAR) imaging is crucial in remote sensing due to its ability to produce high-resolution images regardless of weather conditions or daylight. However, SAR images often suffer from various types of noise, especially speckle noise, which degrades image quality and complicates data analysis. Traditional noise reduction techniques face challenges in balancing noise suppression with the preservation of image details. Recent deep learning approaches, such as U-Net architectures, have made strides in addressing these issues. Simultaneously, quantum computing has emerged as a promising field that can potentially enhance computational methods in image processing. We propose a hybrid classical-quantum U-Net framework that integrates quantum-classical algorithms with tensor networks for improved noise reduction in SAR images. Leveraging the representational capacity of tensor networks and the computational strengths of quantum algorithms, our method aims to surpass the limitations of classical techniques in managing complex noise patterns. In our architecture, certain layers of the U-Net are implemented using quantum circuits specifically optimized for noise suppression. The inclusion of tensor networks enables efficient handling of high-dimensional data within the hybrid model. The idea is to perform extensive experiments on standard SAR datasets to demonstrate that our hybrid U-Net outperforms traditional denoising methods and purely classical deep learning models in both noise reduction and detail preservation. Quantitative assessments using metrics like Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM) would be used for comparison. Moreover, we will evaluate the computational efficiency of our approach, underscoring the potential of quantum computing to expedite complex image processing tasks. This work aims to highlight the feasibility and benefits of integrating quantum computing into practical remote sensing applications. Our hybrid quantum-classical U-Net architecture with tensor networks paves the way for advanced noise reduction techniques in SAR imaging and potentially other domains. Future research will focus on optimizing quantum circuit designs for specific noise types and exploring the scalability of our approach alongside advancements in quantum hardware.
Ternary quantum image processing (TQIP) integrates qutrit-based quantum computing with image representation, offering a promising pathway for efficient image encoding models. This paper introduces Novel Qutrit Quantum Representation (NQQR) model, a scalable and optimized ternary model for representing grayscale and RGB images of size [Formula: see text] in a ternary quantum system. The proposed model encodes pixel positions using the superposition of 2n qutrits and assigns grayscale or color intensity values using 6 qutrits, while an additional color qutrit and an ancilla qutrit are employed to reduce the complexity of multi-controlled N-qutrit gates, thereby lowering the overall quantum cost. Experimental results show that the NQQR achieves a time complexity of [Formula: see text] and the optimized NQQR model significantly reduces quantum cost, with improvements up to [Formula: see text] for grayscale images and [Formula: see text] for RGB images when compared to equivalent non-optimized qubit/qutrit-based encoding models. In addition, the performance of the proposed model is evaluated under quantum depolarizing noise using MSE and PSNR metrics computed between the original and reconstructed images under different noise levels. These results demonstrate the efficiency and scalability of the proposed NQQR model at the representation level and suggest its potential as a building block for future research in TQIP.
Accurate land classification from satellite imagery is essential for applications in agriculture, environmental management, and urban planning. Traditional lightweight models such as MobileNet provide computational efficiency but often struggle to capture the fine-grained spatial features necessary for reliable classification. This study proposes a quantum-augmented MobileNet framework that leverages quantum circuits to enhance feature representation. The methodology involves preprocessing satellite images and encoding them into quantum states using parameterized quantum circuits that apply superposition and entanglement to capture complex spatial relationships. These enriched features are then processed through a streamlined MobileNetV2 architecture optimized for multi-class classification across ten land cover categories. The model is trained and evaluated using the EuroSAT dataset consisting of 27,000 images. Experimental outcomes show that the hybrid quantum-classical approach improves model robustness and classification performance without increasing computational overhead. This integration offers a scalable and efficient solution for real-time remote sensing applications, demonstrating the promise of quantum computing in advancing geospatial intelligence.
Quantum image computing has attracted attention due to its vast storage and faster processing of image data than classical computers. Although classical computing power has grown substantially in the last decade, its abilities have plateaued, struggling to meet the needs of huge datasets. Quantum computers, on the other hand, take advantage of unique qualities like quantum parallelism, superposition, and entanglement to provide faster solutions to complicated problems. Managing huge image data in classical systems necessitates significant memory and hardware resources, which makes quantum computing a possible alternative. Several approaches have arisen for encoding and compressing classical images in the quantum processor. However, one of the most significant limitations is the complexity of quantum state preparation, which translates pixel coordinates to corresponding quantum circuits. Current approaches for representing large-scale images require a huge number of quantum resources including qubit, and qubit connection, which presents significant hurdles. Therefore, this article aims to overview the pixel intensity and state preparation circuit that needs fewer connections, i.e., quantum gates to represent quantum image data, and then explore and design effective compression and processing methods for medium and high-resolution images. This work specializes in emerging quantum models’ potential for enhancing image representation and compression capabilities. It also conducts a comprehensive study of quantum image representation and compression techniques, categorizing existing methods by grayscale and color image types and evaluating their methods, strengths, and limitations. It focuses on crucial difficulties, such as high qubit requirements and complex state preparation, that limit scalability for huge images. This paper also assesses each model’s compression efficacy, which will help future research focus on efficient circuit designs with lower complexity for medium to high-resolution images. It is an essential reference for advancing quantum image processing research since it provides a systematic framework for evaluating quantum image compression and representation algorithms.
We present a hybrid quantum-classical framework designed to achieve utility-scale performance for Quantum Hadamard Edge Detection (QHED) on Noisy Intermediate-Scale Quantum (NISQ) devices. The framework utilizes a Two-Level Decomposition strategy: (1) Problem-Level Decomposition (PLD), which partitions high-resolution real-world data into buffered sub-images, and (2) Circuit-Level Decomposition (CLD), which employs circuit-cutting to reduce complexity for near-term hardware. This approach, combined with a depth-efficient QHED^M decrement gate, achieves a 62% reduction in circuit depth and 93% fewer two-qubit operations. We demonstrate the framework's domain-agnostic utility by processing real-world data from Medical Image Computing (MIC) and Geospatial Information Systems (GIS). Crucially, we investigate the resilience crossover point by comparing a [[3,1,1]] bit-flip repetition code against passive Quantum Error Mitigation (QEM). Our results indicate that for current coherence times, passive mitigation offers a superior utility advantage by bypassing the gate-overhead penalties inherent in active encoding, recovering nearly 99% of the ideal signal.