In this paper we propose a quantum convolutional neural network(QCNN) based chaotic metric classification for chaology scaling-down images in the tensorflow quantum framework. The chaology image is properly downscaled with the measurement of spatial occupancy and granularity introduced by fractal dimension and the dimensionality of the overall input space is reduce before it is fed into the QCNNs quantum circuit for state preparation, quantum convolution and quantum pooling. The experimental results show that QCNN, Hybrid QCNN and Hybrid QCNN with multiple quantum filters have achieved relatively high accuracies on more than 94%, and convergence of these three kinds of QCNN classifier has a small fluctuation which indicates that through this scale transformation the discrepancy among the different quantum hybrid classifiers is minimized to some extent.
In this paper we propose a quantum convolutional neural network(QCNN) based chaotic metric classification for chaology scaling-down images in the tensorflow quantum framework. The chaology image is properly downscaled with the measurement of spatial occupancy and granularity introduced by fractal dimension and the dimensionality of the overall input space is reduce before it is fed into the QCNNs quantum circuit for state preparation, quantum convolution and quantum pooling. The experimental results show that QCNN, Hybrid QCNN and Hybrid QCNN with multiple quantum filters have achieved relatively high accuracies on more than 94%, and convergence of these three kinds of QCNN classifier has a small fluctuation which indicates that through this scale transformation the discrepancy among the different quantum hybrid classifiers is minimized to some extent.
In recent years, there has been a growing interest in leveraging the unique properties of quantum computing to develop novel machine learning algorithms and architectures. This research paper presents an investigation of quantum convolutional neural networks (QCNNs), which leverage the unique properties of quantum computing to potentially improve the accuracy and efficiency of image classification tasks. Specifically, the paper explores three different QCNN architectures, including a pure quantum-based QCNN, a hybrid QCNN with a single quantum convolution layer, and a hybrid convolutional architecture with multiple quantum filters. We tested the models on MNIST dataset and the results of the study demonstrate that hybrid architectures that combine quantum and classical processing are more effective than pure quantum-based architectures in image classification tasks. In particular, the third model, the Hybrid Convolution with Multiple Quantum Filters, achieved the highest test set accuracy of 92.7%. The use of multiple quantum filters in conjunction with a classical neural network resulted in enhanced accuracy and efficiency in image classification tasks, highlighting the potential of hybrid architectures for future applications in machine learning tasks.
Deep learning has achieved significant success in pattern recognition, with convolutional neural networks (CNNs) serving as a foundational architecture for extracting spatial features from images. Quantum computing provides an alternative computational framework, a hybrid quantum-classical convolutional neural networks (QCCNNs) leverage high-dimensional Hilbert spaces and entanglement to surpass classical CNNs in image classification accuracy under comparable architectures. Despite performance improvements, QCCNNs typically use fixed quantum layers without incorporating trainable quantum parameters. This limits their ability to capture non-linear quantum representations and separates the model from the potential advantages of expressive quantum learning. In this work, we present a hybrid quantum-classical-quantum convolutional neural network (QCQ-CNN) that incorporates a quantum convolutional filter, a shallow classical CNN, and a trainable variational quantum classifier. This architecture aims to enhance the expressivity of decision boundaries in image classification tasks by introducing tunable quantum parameters into the end-to-end learning process. Through a series of small-sample experiments on MNIST, F-MNIST, and MRI tumor datasets, QCQ-CNN demonstrates competitive accuracy and convergence behavior compared to classical and hybrid baselines. We further analyze the effect of ansatz depth and find that moderate-depth quantum circuits can improve learning stability without introducing excessive complexity. Additionally, simulations incorporating depolarizing noise and finite sampling shots suggest that QCQ-CNN maintains a certain degree of robustness under realistic quantum noise conditions. While our results are currently limited to simulations with small-scale quantum circuits, the proposed approach offers a potentially promising direction for hybrid quantum learning in near-term applications.
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.
Quantum machine learning is emerging as a promising frontier at the intersection of quantum computing and Artificial Intelligence, offering potential gains in speed, efficiency, and robustness. In this paper, a hybrid Quantum Convolutional Neural Network (QCNN) architecture is presented for visual pattern recognition tasks, leveraging quantum superposition and entanglement for enhanced feature extraction. Unlike previous studies that focus on small-scale or simplified datasets, this study systematically evaluates QCNNs across multiple benchmark datasets and provides a direct comparison with classical CNNs, highlighting practical quantum advantages such as parameter efficiency and noise-resilience. The proposed approach encodes classical image data from benchmark datasets, including70K28×28grayscale images from MNIST and FashionMNIST, and60Kdownsampled grayscale CIFAR-10 images, into quantum states using amplitude encoding, followed by hierarchical quantum convolution and pooling layers implemented via parametrised quantum circuits (PQCs). The model culminates in a classical dense layer for final classification. Experimental results demonstrate that the QCNN achieves competitive performance on low-dimensional grayscale datasets with 93.2 % accuracy on MNIST and 88.5% on Fashion MNIST, though it underperforms on more complex datasets like CIFAR-10 with 67.8 % accuracy due to current quantum hardware limitations. While not yet surpassing classical CNNs in raw accuracy, the proposed QCNN exhibits notable parameter efficiency, noise resilience, and theoretical potential for energy-efficient computation, highlighting its promise for future applications as quantum technologies mature.
While classical convolutional neural networks (CNNs) have revolutionized imageclassification, the emergence of quantum CNNs (QCNNs) presents new opportunities with high-dimensional transformations that are infeasible for classicalCNNs. However, their implementation on current noisy intermediate-scale quantum (NISQ) devices remains challenging due to hardware limitations. In ourresearch, we address this challenge by introducing fragment encoding that significantly reduces the input dimensionality. We show that accuracy improvesas the number of qubits increases from 1 to 4 to 16, providing evidence offavorable scaling. Motivated by this, we developed a primitive QCNN architecture with 49 qubits which is sufficient to directly process 28 × 28 pixelMNIST images, eliminating the need for classical dimensionality reductionpre-processing. Additionally, we propose an automated framework based onexpressibility, entanglement, and complexity characteristics to identify the building blocks of QCNNs, parameterized quantum circuits (PQCs). Our approachdemonstrates advantages in convergence speed with a similar parameter countcompared to both hybrid QCNNs and classical CNNs. We validated our experiments on IBM’s Heron r2 quantum processor, achieving 96.08% accuracy withapproximately 70% faster convergence relative to classical CNNs under identicaltraining conditions. Our results show that simple image-classification tasks canbe realized on real quantum hardware today.
In this paper we propose a MERA inspired ternary unitary circuit feature extraction method based on Quantum Convolutional Neural Network(QCNN) in the TensorFlow quantum framework for binary image classification. The image data is properly downscaled with the ternary unitary circuits before fed into the QCNNs quantum circuit for state preparation, quantum convolution and quantum pooling. High or appropriate entangled state corresponds to the high separated weight function. When entanglement is reduced, the classification result can then be directly obtained from the qubit. We trained quantum classifiers with QCNN and with two hybrid quantum-classical QCNN models and their performance are compared against that of a classic CNN. The results on the breast cancer image dataset show that the proposed QCNN with the proposed feature extraction outperformed the classic CNN in terms of recognition rate 90%-93% over 83%-85% on performance.