This study presents an advanced Quantum Convolutional Neural Network (QCNN) architecture designed to improve classification performance and training stability within the constraints of Noisy Intermediate-Scale Quantum (NISQ) devices. The proposed model incorporates a multi-dimensional quantum feature encoding stage, followed by entanglement-enriched convolutional layers and a refined quantum pooling mechanism. These design innovations increase the circuit’s expressive capacity while reducing susceptibility to barren plateau phenomena, thereby supporting more efficient optimization. Extensive numerical simulations indicate that the model achieves a classification accuracy of 95.2%, with a final loss of 0.24, a low output variance of 0.031, and improved expressibility characterized by a mean fidelity of 0.07. Collectively, these results demonstrate that the enhanced QCNN provides a robust and scalable framework for quantum machine learning tasks suitable for current and near-term quantum hardware.
This study presents an advanced Quantum Convolutional Neural Network (QCNN) architecture designed to improve classification performance and training stability within the constraints of Noisy Intermediate-Scale Quantum (NISQ) devices. The proposed model incorporates a multi-dimensional quantum feature encoding stage, followed by entanglement-enriched convolutional layers and a refined quantum pooling mechanism. These design innovations increase the circuit’s expressive capacity while reducing susceptibility to barren plateau phenomena, thereby supporting more efficient optimization. Extensive numerical simulations indicate that the model achieves a classification accuracy of 95.2%, with a final loss of 0.24, a low output variance of 0.031, and improved expressibility characterized by a mean fidelity of 0.07. Collectively, these results demonstrate that the enhanced QCNN provides a robust and scalable framework for quantum machine learning tasks suitable for current and near-term quantum hardware.
This study presents an advanced Quantum Convolutional Neural Network (QCNN) architecture tailored for improved classification accuracy and enhanced training stability within the constraints of Noisy Intermediate-Scale Quantum (NISQ) devices. The proposed model incorporates a multi-dimensional quantum feature encoding stage, followed by entanglement-enriched convolutional layers and a refined quantum pooling mechanism. These design innovations substantially increase the circuit’s expressive capacity while reducing susceptibility to barren plateau phenomena, thereby supporting more efficient optimization. Extensive numerical simulations indicate that the model achieves a classification accuracy of 95.2%, alongside a final loss of 0.24, low output variance of 0.031, and improved expressibility characterized by a mean fidelity of 0.07. Collectively, these results demonstrate that the enhanced QCNN offers a robust and scalable framework for quantum machine learning tasks suitable for current and near-term quantum hardware. Also, modern applications from qunatum devices with the abovementioned result is met and leads to practical systems.
Image classification plays a vital role in large-scale data analysis, especially in object recognition tasks using advanced Deep Learning (DL) frameworks. However, the growing complexity and computational demands of modern DL models have introduced challenges related to scalability and efficiency. Quantum Computing (QC) has emerged as a promising alternative, capable of addressing these limitations by leveraging the principles of Quantum Machine Learning (QML). However, many existing QML models require a large number of qubits, which poses limitations within the current Noisy Intermediate-Scale Quantum (NISQ) era. This work introduces a novel Adaptive Quantum Convolutional Neural Network (AQCNN) designed for efficient and scalable image classification within the constraints of NISQ devices. Addressing the limitations of existing QML approaches, particularly the high qubit requirements, AQCNN incorporates a resource-efficient quantum convolutional layer that performs localized quantum filtering using parameterized quantum circuits. A classical preprocessing layer encodes input features to reduce qubit load, followed by quantum embedding and hybrid quantum-classical layers that optimize feature extraction and classification performance. The model leverages an adaptive quantum convolution strategy, minimizing quantum gate depth and circuit complexity while preserving critical spatial hierarchies in image data. Evaluated on benchmark datasets, AQCNN achieved 95.88% accuracy on MNIST and 95.68% on FMNIST, outperforming comparable QML architectures. Additionally, the model supports scalable execution through parallel quantum circuit arrays, enabling practical deployment on current quantum hardware. This architecture demonstrates a significant advance in quantum-assisted image classification, balancing performance with qubit and gate efficiency. The integration of adaptive quantum convolution and hybrid processing not only enhances classification accuracy but also provides a viable path forward for deploying QML solutions under realistic hardware constraints.
As image datasets grow in scale and complexity, classical convolutional neural networks (CNNs) increasingly face limitations in computational efficiency, scalability, and generalisation in low-data regimes. Quantum machine learning (QML) offers a promising alternative, with quantum convolutional neural networks (QCNNs) exploiting quantum parallelism and entanglement for feature extraction. This paper introduces the Novel Quantum Convolutional Neural Network (No-QCNN), which differs from recent hybrid QCNN/QCQ-CNN architectures by employing an end-to-end quantum convolution-pooling pipeline without inserting an intermediate classical CNN block. No-QCNN is a hybrid quantum-classical model that employs a variational quantum classifier (VQC) optimised via the COBYLA algorithm and is designed for binary and multiclass classification of low-resolution colour images on near-term quantum devices. A key innovation is a problem-specific quantum feature map that pre-processes each image into a structured three-dimensional block-matrix representation, jointly encoding pixel colour (R, G, B) and spatial position before mapping this information into a hierarchical ZZFeatureMap. This encoding captures spatial-chromatic correlations at shallow circuit depth, making it compatible with noisy intermediate-scale quantum (NISQ) constraints. The model is implemented using IBM-Qiskit on a local quantum simulator and benchmarked against a classical CNN. For a six-class classification task on a 50-image dataset, No-QCNN achieves a validation accuracy of 82.05%, substantially outperforming the classical CNN's 40.00%, indicating improved generalisation and reduced overfitting in low-data, multiclass settings. Conversely, for a simpler binary classification task, the classical CNN achieves perfect validation accuracy of 100%, surpassing the No-QCNN's 89.7%. We further observe that No-QCNN performance declines with increasing dataset size and training time due to limited circuit expressibility, data-encoding overhead, and finite-sampling noise inherent to NISQ models. Overall, these results position No-QCNN as a complementary framework best suited to low-data, correlation-rich classification tasks, defining a realistic niche for quantum-enhanced artificial vision and quantum perception in the NISQ era.
This paper presents an improved Quantum Convolutional Neural Network (QCNN) architecture that overcomes key limitations of current quantum-classical hybrid models for image classification. To address challenges in expressivity and feature extraction on noisy intermediate-scale quantum (NISQ) devices, we propose two key innovations: (1) Gate Encoding Optimization, which reduces quantum bit (qubit) usage while retaining spatial information, and (2) Quantum Channel Attention (QCA), a novel attention mechanism tailored for hybrid QCNNs which utilizes measurement-driven feedback to capture inter-channel dependencies. Evaluated on the MNIST dataset, our model achieves 95.68 % accuracy—on par with classical CNNs (95.73 %) and significantly better than a baseline QCNN (93.23%)—while reducing parameters by 49.6 %. These results demonstrate the proposed model's effectiveness and practicality for quantum machine learning in the NISQ era.
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.
Quantum convolutional neural networks (QCNNs) are emerging as promising models in quantum machine learning, particularly for image classification and computer vision tasks. Recent developments include hybrid classical–quantum architectures, advanced quantum encoding methods, and novel circuit designs that improve data processing on Noisy Intermediate-Scale Quantum (NISQ) devices. However, practical implementation remains challenging due to circuit complexity, gate count, qubit connectivity, and hardware noise, which limit scalability and performance. Consequently, quantum circuit optimization has become essential for reducing resource requirements and improving classification accuracy. This study presents a systematic literature review of 40 research papers published between 2014 and 2025. The review covers QCNNs together with closely related quantum neural network (QNN) models and quantum circuit optimization studies, since circuit-optimization techniques are frequently developed for QNNs more broadly rather than for QCNN architectures in isolation. Within this scope, it examines network architectures, encoding strategies, application domains, and optimization techniques, with particular attention to heuristic and metaheuristic approaches such as genetic algorithms and evolutionary strategies. The findings highlight growing trends in hybrid quantum–classical integration, the widespread adoption of metaheuristic optimization, and the importance of multi-objective frameworks adapted to quantum hardware constraints. Finally, the review identifies key research gaps and future directions for practical QCNN deployment on near-term quantum devices.