Earth observation (EO) is increasingly driven by large-scale remote sensing (RS) data, acquired from satellite and airborne platforms across diverse temporal and spatial resolutions. These datasets are characterized not only by volume, but by complex properties such as multi-source heterogeneity, high dimensionality, nonlinear feature distributions, and spatio-temporal variability. Processing such data at operational scale introduces significant algorithmic and computational challenges, particularly in high resolution environmental monitoring and planetary-scale inference tasks. Quantum computing (QC) offers a computational paradigm fundamentally different from classical computing, leveraging the principles of quantum mechanics to perform operations in high-dimensional state spaces. This theoretical advantage makes QC a compelling candidate for selected EO tasks, especially those involving combinatorial optimization and learning tasks. However, the limited qubit fidelity and scale of current quantum hardware constrain their direct applicability to operational applications in Earth observation. This PhD thesis investigates the application of annealing-based and circuit-based quantum algorithms to EO, as well as the integration of quantum algorithms with classical devices, such as those in high-performance computing (HPC) environments. It examines acquisition scheduling and data classification tasks within EO workflows. Experiments assess practical algorithmic benefits, computational scalability, and constraints imposed by hybrid quantum-classical execution. Results demonstrate that quantum modules, when carefully embedded into HPC architectures, can enhance selected stages of EO pipelines, specifically using quantum machine learning and quantum optimization approaches.
Earth observation (EO) is increasingly driven by large-scale remote sensing (RS) data, acquired from satellite and airborne platforms across diverse temporal and spatial resolutions. These datasets are characterized not only by volume, but by complex properties such as multi-source heterogeneity, high dimensionality, nonlinear feature distributions, and spatio-temporal variability. Processing such data at operational scale introduces significant algorithmic and computational challenges, particularly in high resolution environmental monitoring and planetary-scale inference tasks. Quantum computing (QC) offers a computational paradigm fundamentally different from classical computing, leveraging the principles of quantum mechanics to perform operations in high-dimensional state spaces. This theoretical advantage makes QC a compelling candidate for selected EO tasks, especially those involving combinatorial optimization and learning tasks. However, the limited qubit fidelity and scale of current quantum hardware constrain their direct applicability to operational applications in Earth observation. This PhD thesis investigates the application of annealing-based and circuit-based quantum algorithms to EO, as well as the integration of quantum algorithms with classical devices, such as those in high-performance computing (HPC) environments. It examines acquisition scheduling and data classification tasks within EO workflows. Experiments assess practical algorithmic benefits, computational scalability, and constraints imposed by hybrid quantum-classical execution. Results demonstrate that quantum modules, when carefully embedded into HPC architectures, can enhance selected stages of EO pipelines, specifically using quantum machine learning and quantum optimization approaches.
Quantum machine learning (QML) is an emerging interdisciplinary field that employs quantum computing (QC) principles to address complex computational challenges. Traditional machine learning (ML) and deep learning (DL) models face major challenges in Earth observation (EO) due to the increasing volume and complexity of data. These challenges include computational intensity, energy consumption, and data management constraints. QML emerges as a promising paradigm for overcoming these barriers through quantum phenomena such as superposition, entanglement, and interference. This review aims at offering a detailed analysis of the state of the art and new trends in QML application to EO. This study methodically examines key advancements in QML for EO (QML4EO) through an exploration of the fundamental concepts of QML. The proposed review methodology involved systematic searches across prominent scientific databases, based on our extensive knowledge on the topic, using carefully formulated queries to ensure broad coverage and high relevance. In addition, this work addresses the changing institutional and geographic environment of QML4EO research, highlighting important centers of contribution and the important role of programs such as the IEEE Geoscience and Remote Sensing Society (GRSS) Quantum Earth Science and Technology (QUEST) Technical Committee. To transform QML from a specialized research area into a complementary and essential tool for advancing EO, this article critically analyzes current limitations and provides an outlook on future directions, highlighting the need for reliable hardware, improved algorithmic design, and standardized protocols.
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.
This article examines the current status of quantum computing in Earth observation (EO) and satellite imagery. We analyze the potential limitations and applications of quantum learning models when dealing with satellite data, considering the persistent challenges of profiting from quantum advantage and finding the optimal sharing between high-performance computing (HPC) and quantum computing (QC). We then assess some parameterized quantum circuit models transpiled into a Clifford+T universal gate set. The T-gates shed light on the quantum resources required to deploy quantum models, either on an HPC system or several QC systems. In particular, if the T-gates cannot be simulated efficiently on an HPC system, we can apply a quantum computer and its computational power over conventional techniques. Our quantum resource estimation showed that quantum machine learning (QML) models, with a sufficient number of T-gates, provide the quantum advantage if and only if they generalize on unseen data points better than their classical counterparts deployed on the HPC system and they break the symmetry in their weights at each learning iteration like in conventional deep neural networks. We also estimated the quantum resources required for some QML models as an initial innovation. Lastly, we defined the optimal sharing between an HPC+QC system for executing QML models for hyperspectral satellite images. These are a unique dataset compared to other satellite images since they have a limited number of input qubits and a small number of labeled benchmark images, making them less challenging to deploy on quantum computers.
Quantum computing has emerged as a transformative paradigm, leveraging principles of quantum mechanics to tackle computationally intractable problems. This manuscript explores the potential of quantum computing for solving complex engineering problems—specifically optimization, simulation, and data analysis tasks—using technologies and methodologies available up to 2022. We examine both gate-based and quantum annealing approaches, outline experimental frameworks, and compare performance against classical counterparts. Results from simulated benchmark problems demonstrate promising speedups in select cases, though practical adoption is constrained by hardware limitations such as qubit coherence times and noise. The study concludes by highlighting avenues for integrating hybrid quantum-classical workflows in engineering, along with a discussion of current constraints and future directions. Over the past decade, research efforts have focused on understanding how quantum phenomena like superposition and entanglement can be harnessed to realize computational advantages for specific problem classes. In particular, combinatorial optimization problems—ubiquitous in engineering design and logistics—offer fertile ground for quantum algorithms such as the Quantum Approximate Optimization Algorithm (QAOA) and quantum annealing. Additionally, quantum simulation techniques, including the Variational Quantum Eigensolver (VQE), promise to accelerate the modeling of physical systems at the molecular and materials level, potentially informing the design of next-generation structural and functional materials. Beyond raw algorithmic performance, this paper carefully assesses the end‑to‑end workflow, accounting for overheads in problem encoding, error mitigation, and classical post‑processing. We also present a detailed analysis of resource utilization—quantified by qubit counts, circuit depths, and annealing schedules—highlighting trade‑offs between solution quality and execution time. Ultimately, our findings underscore that, while pure quantum speedups remain rare in 2022, hybrid approaches harnessing both quantum and classical resources can yield practical benefits for small‑to‑medium scale engineering tasks. This expanded evaluation provides a roadmap for researchers and practitioners interested in applying quantum computing to real‑world engineering challenges.
In recent years, quantum computing, as a potential way to break the bottleneck of Moore's Law, has gradually shown its unique advantages in the field of machine learning. Not only can it bring exponential or polynomial acceleration in some specific tasks, it also provides entirely new ways of representing and processing information. Focusing on the core theme of application and method innovation of quantum machine learning in structured data, this thesis systematically studies the theoretical models and practical applications of quantum computing in many sub-fields such as agricultural remote sensing, graph neural networks, and hyperdimensional computing, and explores the adaptability, scalability, and effectiveness of quantum models in real-world tasks. Firstly, in the cropland classification and yield prediction task, this study identifies that traditional neural networks tend to underperform when facing imbalanced and small-sample remote sensing data, often misclassifying minority crop types. To address this, we propose a quantum-classical hybrid recognition framework that integrates quantum feature mapping with classical neural networks. After incorporating quantum feature encoding, we observe a significant improvement in classification performance, especially in cases involving subtle field boundaries and heterogeneous textures. The quantum-enhanced model demonstrates superior accuracy, robustness, and generalization compared to its classical counterpart, validating the advantage of leveraging quantum representations in real-world agricultural datasets. The second part focuses on the learning of graph structure data and proposes to embed the Continuous Time Quantum Walk (CTQW) into the Graph Neural Network (GNN) architecture. This method not only preserves the global interference characteristic of quantum states but also realizes the efficient modeling of graph topology through the spectral graph methods. Experiments show that the proposed method is superior to many classical graph learning models in tasks such as node classification and graph structure inference. The third and fourth components of this thesis collectively explore quantum approaches to hyperdimensional computing (HDC), establishing a progressive transition from native quantum-state-based implementation to quantum-enhanced HDC strategies. Initially, we investigate the intrinsic mathematical correspondence between quantum states and high-dimensional hypervectors, and propose a novel quantum-state-based high-dimensional encoding method. This method is realized through quantum circuits capable of generating hypervectors with controllable entropy, thereby implementing essential HDC operations, such as classification and memory reconstruction, on real quantum hardware. This marks the first successful migration of core HDC mechanisms into a fully quantum-native framework. Building upon this foundation, we further propose the first quantum-enhanced HDC strategy, which does not rely solely on quantum-native implementations but rather utilizes key quantum features to boost the efficiency and expressivity of conventional HDC systems. Specifically, we introduce mechanisms such as quantum-reference-state-based binding, using density matrices to represent superclass states, and quantum state tomography for robust feature extraction. These innovations significantly enhance the resilience and performance of HDC under high-noise and high-dimensional sensing environments. The resulting models achieve superior accuracy and robustness across a variety of multi-class classification tasks, demonstrating the practical benefits of integrating quantum computation into the broader HDC paradigm. In this thesis, experimental validation is carried out in multiple real and simulation environments, including Qiskit and Paddle Quantum platforms, and a complete closed loop was achieved from theoretical derivation, quantum circuit design to end-to-end experimental evaluation. The results show that quantum methods can expand the expressive power of classical models and become an important tool to deal with large-scale, structured data problems in the future. The work in this thesis provides methodological support for quantum machine learning from theory to application, and also expands the research boundaries of high-dimensional computation and graph learning in the quantum background.
Quantum machine learning (QML) is receiving increasing attention as a potential tool for addressing the computational challenges inherent in agricultural remote sensing, where multimodal Earth observational satellite, soil, and climate datasets must be processed at scale. We review recent developments in quantum–classical hybrid modeling for core remote-sensing tasks, including crop type classification, yield estimation, cloud detection, and time series forecasting. Researchers typically combine classical feature extractors with shallow variational circuits or quantum kernels to accommodate the limited qubit counts and noise levels of contemporary quantum devices. We summarize performance trends reported in the literature, emphasizing the role of classical preprocessing, compact quantum circuits, and rigorous benchmarking against strong classical baselines. We consolidate the reviewed studies in a structured comparison covering task, dataset, qubit count, encoding, execution platform, and reported performance relative to classical baselines. The current hardware and data limitations that restrict current QML applications associated with state preparation, circuit depth, and encoding of agricultural datasets are discussed. Near-term prospects for QML in agriculture can involve small-scale hybrid models for multi-class crop mapping and short horizon sequence prediction, while longer-term opportunities may encompass quantum accelerated image segmentation, data fusion, and large scale optimization. Current evidence indicates that QML performance at best matches strong classical baselines, with the most plausible near-term utility arising in narrow settings such as severely label-limited learning and QUBO-structured optimization. Future gains will depend jointly on hardware maturation, data encoding strategies, algorithm design, and integration with classical AI systems.