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
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
Abstract - We present a comprehensive study exploring quantum machine learning (QML) approaches for plant disease detection from leaf images and compare them against well-established classical counterparts. Specifically, we implement and analyze Quantum Support Vector Machines (QSVMs) vs classical SVMs, and Quantum Convolutional Neural Networks (QCNNs) vs classical CNNs. Using the widely used PlantVillage and complementary field datasets, we describe image preprocessing, classical baseline architectures, quantum data-encoding strategies, circuit-level QSVM and QCNN designs for near-term quantum devices, and hybrid training procedures. Where possible, we review literature-reported performance and propose a reproducible experimental pipeline for empirical evaluation on simulated/noisy quantum backends. We discuss expected strengths and limitations of quantum approaches (expressivity, kernel advantages, resource constraints), provide detailed evaluation metrics and ablations, and propose directions for real-device experiments and field deployment. Key takeaways: QSVM/quantum-kernel methods can provide superior separability on certain feature maps and small-to-medium-sized datasets, while QCNNs show promise as compact feature extractors for hybrid pipelines — but both approaches currently require careful circuit design and error-mitigation to outperform well-tuned classical models in realistic field settings. Key Words: QCNN, Plant Disease, SVM, CNN, QSVM
The integration of quantum computing principles with classical deep learning architectures has emerged as a promising direction for advancing image recognition systems beyond the limitations of purely classical convolutional neural networks (CNNs). This study proposes and analyzes a hybrid quantum–classical deep learning framework based on quanvolutional neural network (QNN) principles, where quantum circuits are embedded within convolutional feature extraction layers to enhance representation capability. The research investigates how quantum-enhanced filters, particularly quanvolutional layers, contribute to improved feature diversity, non-linear transformation capacity, and classification accuracy in image recognition tasks. Building upon established convolutional architectures and quantum machine learning theories, the proposed framework integrates quantum random circuit encoding with classical deep neural networks to evaluate performance gains in comparison to conventional CNN models. The study further incorporates theoretical insights from hybrid quantum-classical learning systems, emphasizing the role of parameterized quantum circuits in feature mapping and optimization stability. The methodology is grounded in NISQ-era quantum computing constraints and leverages hybrid optimization strategies to ensure computational feasibility. Experimental evaluation demonstrates that quanvolution-based models can provide improved robustness in feature extraction, particularly in high-dimensional image spaces, while maintaining competitive computational efficiency. However, scalability limitations and noise sensitivity in quantum circuits remain key challenges. Overall, this work contributes to the growing field of quantum-enhanced machine learning by providing a structured performance analysis of hybrid quanvolutional architectures and highlighting their potential role in next-generation intelligent visual recognition systems.
Classical convolutional neural networks (CNNs) have achieved notable success in image classification but face challenges in scalability, interpretability, and computational cost. With the growing availability of Noisy Intermediate-Scale Quantum (NISQ) devices, hybrid quantum-classical models offer a promising alternative by leveraging quantum parallelism and entanglement. In this work, we present a hybrid Quantum Convolutional Neural Network (QCNN) that integrates quantum convolutional layers into classical image classification pipeline for the MNIST dataset. Our architecture features a modular, low-depth Quantum Kernel Convolution (QKC) design suitable for near-term hardware. We implement the system using Qiskit and evaluate in terms of classification accuracy, circuit efficiency, and integration feasibility. Our results demonstrate that (i) the hybrid QCNN achieves similar accuracy to classical models while reducing the number of classical parameters, and (ii) quantum layers can successfully perform dimensionality reduction through entanglement-based pooling. This study highlights the potential of hybrid QCNN as a practical quantum-enhanced model for real-world image recognition tasks.
Satellite image classification is a crucial task in remote sensing for applications such as land cover mapping, environmental monitoring, and resource management. While classical machine learning models such as SVMs achieve strong accuracy, they are often sensitive to noise, parameter-heavy, and less efficient when training data is scarce. In this work, we propose a hybrid quantum–classical pipeline for binary land cover classification on the EuroSAT dataset, combining a convolutional autoencoder for feature extraction with a Variational Quantum Classifier (VQC) inspired by quantum convolutional neural networks. The proposed QCNN–VQC achieves 90.0% accuracy, but more importantly outperforms baselines on F1-score (0.9428 after threshold tuning), AUROC (0.9819), and AUPRC (0.9812), while requiring fewer parameters. The model also demonstrates superior robustness to label noise and attains exceptionally high recall for the SeaLake class (97.8%), ensuring reliable detection of water bodies. These results highlight the promise of quantum machine learning for practical remote sensing tasks, especially in noisy or low-data regimes.
Abstract Quantum Machine Learning (QML) has emerged as a promising paradigm that leverages the principles of quantum mechanics—such as superposition and entanglement—to overcome the limitations of classical machine learning in handling complex, high-dimensional data. However, practical challenges such as limited coherence times of qubits and the integration of quantum circuits with classical architectures have hindered the full realization of QML’s potential. To address these challenges, this paper proposes a novel hybrid architecture, termed the Classical-Quantum Hybrid Convolutional Neural Network (CQHCNN), which integrates quantum-inspired layers into conventional deep learning frameworks. These quantum layers utilize quantum information processing to extract complex features, while the classical components perform feature extraction and classification. This paper evaluates the performance of our model on several benchmark datasets, including MNIST, Fashion-MNIST, CIFAR-10 and COVID-19 Radiography, demonstrating its ability to outperform some other Quantum Convolutional Neural Networks (QCNNs) in terms of many metrics. Experimental results indicate that the hybrid model achieves significant improvements in classification accuracy and computational efficiency, highlighting the potential of quantum-inspired techniques to enhance classical machine learning models.
Noisy Intermediate-Scale Quantum (NISQ) devices have led to the creation of hybrid quantum-classical algorithms, which have provided an innovative design to improve classical machine learning. This article focuses on a review of Hybrid Quantum-Classical Neural Networks (H-QCNNs) in image classification, which is an emerging area that attempts to exploit the representational ability of parameterized quantum circuits (PQCs) and the proven feature extraction capabilities of classical deep learning. We empirically examine the current tendencies in architecture, such as quanvolutional networks, sequential models, and variational quantum classifiers, and critically assess their performance on standard and custom data. The literature analysis shows a large performance difference between noiseless simulation and execution on real hardware, indicating the highly problematic aspects of scaling beyond binary tasks, quantum noise, and a lack of standardized benchmarking. Moreover, we mention such innovative contributions as the small H-QNN model that offers comparable accuracy, yet meets strict NISQ requirements. Last but not least, the current review offers a systematic taxonomy of H-QCNN architectures, a comparative study of methodology options, and a clear roadmap to future research to reach a workable quantum advantage in computer vision. It is a resource to the beginners as well as a reference to the professionals working in the field, so the field would be moved to more powerful, scalable, and hardware-conscious ways of implementation.