Hyperspectral change detection (HCD) is a critical technique for monitoring changes on the Earth's surface over time. Empowered by the spatial relation (captured by the graph learning) and the quantum deep network (QUEEN), precise detection of environmental changes is achieved in this work. With the growing attention on quantum computing technology, highly innovative hybrid quantum-classical QUEENs have been developed in recent years for various hyperspectral remote sensing applications. During the feature extraction stage, quantum modules and classical modules are employed to extract features at the pixel level and superpixel level, respectively. By fusing these two fundamentally different types of feature information, the dual-branch network structure achieves accurate HCD results. Moreover, the proposed highly entangled QUEEN module has mathematically provable quantum expressibility, which ensures a lightweight quantum architecture while preserving exceptional classification performance. After quantum/classical feature extraction, the features are fed into another quantum classifier to decide whether the hyperspectral pixels are changed or not. Experiments conducted on real-world hyperspectral datasets demonstrate the superiority of the proposed quantum-graph network (QGNet), suggesting future investigators explore hybrid quantum-classical learning methods.
Hyperspectral change detection (HCD) is a critical technique for monitoring changes on the Earth's surface over time. Empowered by the spatial relation (captured by the graph learning) and the quantum deep network (QUEEN), precise detection of environmental changes is achieved in this work. With the growing attention on quantum computing technology, highly innovative hybrid quantum-classical QUEENs have been developed in recent years for various hyperspectral remote sensing applications. During the feature extraction stage, quantum modules and classical modules are employed to extract features at the pixel level and superpixel level, respectively. By fusing these two fundamentally different types of feature information, the dual-branch network structure achieves accurate HCD results. Moreover, the proposed highly entangled QUEEN module has mathematically provable quantum expressibility, which ensures a lightweight quantum architecture while preserving exceptional classification performance. After quantum/classical feature extraction, the features are fed into another quantum classifier to decide whether the hyperspectral pixels are changed or not. Experiments conducted on real-world hyperspectral datasets demonstrate the superiority of the proposed quantum-graph network (QGNet), suggesting future investigators explore hybrid quantum-classical learning methods.
Hyperspectral image (HSI) classification is a critical task in the field of remote sensing. Existing deep learning models have made significant progress in HSI classification, but often suffer from parameter redundancy and insufficient noise robustness when handling the high-dimensional nonlinear spatial-spectral information. Recently, quantum computing have garnered significant attention in modeling higher-order correlations with remarkable parameter efficiency. Inspired by this, we propose a QUantum-Enhanced nEural Network (QueenNet) for HSI classification. QueenNet comprises three primary modules, including Quantum Encoding (QE), Quantum Convolution (QC), and Quantum Measurement (QM). Specifically, QE maps HSI into an hyper high-dimensional Hilbert space, encoding it as a quantum states. QC performs HSI feature extraction and transformation using quantum circuits composed of parameterized quantum gate sequences, which effectively enhances the model's ability to capture joint spatial-spectral features while suppressing spectral noise. QM introduces nonlinear feature extraction operations and decodes the HSI features of quantum states back into classic interpretable probability distributions for classification. QueenNet effectively alleviate the limitations of existing HSI classification deep learning models by leveraging the high-dimensional feature representation advantages of quantum computing, introducing a novel paradigm to HSI classification. Extensive experiments on three benchmark datasets demonstrate that QueenNet achieves an order-of-magnitude reduction in model parameters while surpassing the classification performance of state-of-the-art methods.
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.
Remote sensing image classification is a fundamental task for Earth observation and environmental monitoring. However, conventional convolutional neural networks (CNNs) are limited by computational capacity and struggle to efficiently process the rapidly growing volume of remote sensing data. To address this limitation, we propose HAQCCN (Hybrid Asymmetric Quantum–Classical Convolutional Network), a novel hybrid architecture that integrates quantum computation into the classical convolutional framework through asymmetric quantum convolutional circuits. In HAQCCN, the asymmetric quantum circuits enable a limited number of qubits to process more classical data while maintaining excellent feature extraction capability. Experiments conducted on the IBM Qiskit platform using the Overhead-MNIST, PatternNet, and RSI-CB256 datasets demonstrate that HAQCCN outperforms conventional CNNs and existing quantum models. Furthermore, we systematically investigate the effects of encoding schemes, the number of quantum convolutional kernels, and the number of qubits on model performance, confirming the effectiveness and scalability of the proposed method for remote sensing image classification.
Monitoring deforestation accurately and on a large scale is a vital environmental issue, increasingly dependent on analyzing large amounts of satellite images. Although traditional deep learning models like Convolutional Neural Networks (CNNs) have shown effectiveness, they are nearing their limits in terms of performance and computational capacity as data volumes continue to grow. Quantum Machine Learning (QML) offers a revolutionary alternative by utilizing quantum computing principles to identify complex patterns in high-dimensional feature spaces that classical methods cannot handle. This paper thoroughly reviews circuit-based hybrid quantum-classical models, which are suitable for current Noisy Intermediate-Scale Quantum (NISQ) devices, and assesses their use in classifying land use and land cover from satellite data. We examine and contrast two leading architectures: the Quantum Convolutional Neural Network (QCNN), which incorporates a quantum processing layer into a classical network, and the Neural Quantum Kernel (NQK) approach, which employs a quantum circuit to create a robust feature kernel for a classical Support Vector Machine (SVM). The review emphasizes that the effectiveness of these models is greatly affected by the incorporation of quantum entanglement in the circuit design, and they can achieve accuracy levels comparable to or better than the most advanced classical models with significantly reduced complexity. By synthesizing the current state of these emerging technologies, this review highlights the substantial potential of hybrid quantum-classical systems to enhance real-time environmental monitoring, while also identifying the main challenges and future research directions in this promising field
Image classification is a fundamental task in deep learning, and recent advances in quantum computing have generated significant interest in quantum neural networks. Traditionally, Convolutional Neural Networks (CNNs) are employed to extract image features, while Multilayer Perceptrons (MLPs) handle decision making. However, parameterized quantum circuits offer the potential to capture complex image features and define sophisticated decision boundaries. In this paper, we present a novel Hybrid Quantum–Classical Neural Network (H-QNN) for image classification, and demonstrate its effectiveness using the MNIST dataset. Our model combines quantum computing with classical supervised learning to enhance classification accuracy and computational efficiency. In this study, we detail the architecture of the H-QNN, emphasizing its capability in feature learning and image classification. Experimental results demonstrate that the proposed H-QNN model outperforms conventional deep learning methods in various training scenarios, showcasing its effectiveness in high-dimensional image classification tasks. Additionally, we explore the broader applicability of hybrid quantum–classical approaches in other domains. Our findings contribute to the growing body of work in quantum machine learning, and underscore the potential of quantum-enhanced models for image recognition and classification.
Quantum machine intelligence starts showing its impact on satellite remote sensing (SRS). Also, recent literature exhibits that quantum generative intelligences encompass superior potential than their classical counterpart, motivating us to develop quantum generative adversarial networks (GANs) for SRS. However, existing quantum GANs are restricted by the limited quantum bit (qubit) resources of current quantum computers and process merely a small 2 × 2 grayscale image, far from being applicable to SRS. Recently, the novel concept of hybrid quantum-classical GAN, a quantum generator with a classical discriminator, has upgraded the order to 28 × 28 (still grayscale), whereas it is still insufficient for SRS. This motivates us to design a radically new hybrid framework, where both generator and discriminator are hybrid architectures. We demonstrate this feasibility, leading to a breakthrough of processing 128×128 hyperspectral images for SRS. Specifically, we design the quantum part with mathematically provable quantum full expressibility (FE) to address core signal processing tasks, wherein the FE property allows the quantum network to realize any valid quantum operator with appropriate training. The classical part, composed of convolutional layers, treats the read-in (compressing the optical information into limited qubits) and read-out (addressing the quantum collapse effect) procedures. The proposed innovative hybrid quantum GAN, named “Hyperspectral Knot-like IntelligeNt dIscrimiNator and Generator” (HyperKING), where “knot” partly symbolizes the quantum entanglement and partly the compressed quantum domain in the central part of the network architecture. HyperKING significantly surpasses the classical approaches in hyperspectral tensor completion, mixed noise removal (about 3dB improvement), and blind source separation results.