Abstract Current era is witnessing rapid growth in data and fast data processing methods. In the past few decades, artificial intelligence has matured exponentially and has been applied in many areas such as medicine, transport, finance, and many more. Classical machine learning methods are resource intensive and usually training models takes time. Quantum machine learning can be a alternate solution to this problem and can be used to solve complex data problems. Quantum machine learning utilizes parallel computing capabilities of quantum computers. Quantum machine learning methods can be used for image classification, which is an important task in data science. Here, we have proposed a quantum convolutional neural network for color image classification, where we have applied a quantum convolutional and pooling layers to reduce the number of features without losing crucial information. We have used the proposed method to classify color image of the CIFAR-10 dataset. We have compared the validation accuracies and loss during training for both classical and quantum convolutional neural network. Our results show that we have achieved similar accuracy with less number of trainable parameters. We believe that this study will open new avenue for high dimensional data classification using quantum convolutional neural network in current and beyond NISQ era.
Abstract Current era is witnessing rapid growth in data and fast data processing methods. In the past few decades, artificial intelligence has matured exponentially and has been applied in many areas such as medicine, transport, finance, and many more. Classical machine learning methods are resource intensive and usually training models takes time. Quantum machine learning can be a alternate solution to this problem and can be used to solve complex data problems. Quantum machine learning utilizes parallel computing capabilities of quantum computers. Quantum machine learning methods can be used for image classification, which is an important task in data science. Here, we have proposed a quantum convolutional neural network for color image classification, where we have applied a quantum convolutional and pooling layers to reduce the number of features without losing crucial information. We have used the proposed method to classify color image of the CIFAR-10 dataset. We have compared the validation accuracies and loss during training for both classical and quantum convolutional neural network. Our results show that we have achieved similar accuracy with less number of trainable parameters. We believe that this study will open new avenue for high dimensional data classification using quantum convolutional neural network in current and beyond NISQ era.
Abstract Image classification, a pivotal task in multiple industries, faces computational challenges due to the burgeoning volume of visual data. This research addresses these challenges by introducing two quantum machine learning models that leverage the principles of quantum mechanics for effective computations. Our first model, a hybrid quantum neural network with parallel quantum circuits, enables the execution of computations even in the noisy intermediate-scale quantum era, where circuits with a large number of qubits are currently infeasible. This model demonstrated a record-breaking classification accuracy of 99.21% on the full MNIST dataset, surpassing the performance of known quantum–classical models, while having eight times fewer parameters than its classical counterpart. Also, the results of testing this hybrid model on a Medical MNIST (classification accuracy over 99%), and on CIFAR-10 (classification accuracy over 82%), can serve as evidence of the generalizability of the model and highlights the efficiency of quantum layers in distinguishing common features of input data. Our second model introduces a hybrid quantum neural network with a Quanvolutional layer, reducing image resolution via a convolution process. The model matches the performance of its classical counterpart, having four times fewer trainable parameters, and outperforms a classical model with equal weight parameters. These models represent advancements in quantum machine learning research and illuminate the path towards more accurate image classification systems.
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.
Multiclass classification is of great interest for various applications, for example, it is a common task in computer vision, where one needs to categorize an image into three or more classes. Here we propose a quantum machine learning approach based on quantum convolutional neural networks for solving the multiclass classification problem. The corresponding learning procedure is implemented via TensorFlowQuantum as a hybrid quantum-classical (variational) model, where quantum output results are fed to the softmax activation function with the subsequent minimization of the cross entropy loss via optimizing the parameters of the quantum circuit. Our conceptional improvements here include a new model for a quantum perceptron and an optimized structure of the quantum circuit. We use the proposed approach to solve a 4-class classification problem for the case of the MNIST dataset using eight qubits for data encoding and four ancilla qubits; previous results have been obtained for 3-class classification problems. Our results show that the accuracy of our solution is similar to classical convolutional neural networks with comparable numbers of trainable parameters. We expect that our findings will provide a new step toward the use of quantum neural networks for solving relevant problems in the NISQ era and beyond.
Technological developments in machine learning have opened up new avenues of advancement almost in every sector. The main challenge is communicating effectively with machines and enhancing their classification abilities. Convolutional Neural Network (CNN) is a deep learning technique in pattern recognition that effectively utilizes data correlation information. In contrast, CNN can only train effectively if the data or model dimension is manageable. Quantum CNN (QCNN) offers a new approach to this problem using quantum computing techniques to enhance the functionality of the classical CNN model. This research article provides an overview of the implementation of deep learning with quantum computing models for image classification. The paper also discusses the modelling approach for CNN in classical and quantum domains. In this paper, we present a model for multiclass image classification using the traditional CNN and the CNN with the quantum layer using the MNIST and CIFAR-10 datasets. The simulation results show that the proposed QCNN model has lower loss and comparatively more enhanced performance than the traditional CNN model. Also, the proposed model performs relatively better when compared with recent state-of-the-art approaches.
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.
From self-driving cars to medical diagnoses, machine learning (ML) has revolutionized our world in the last few decades. Additionally, Quantum Computing has considerable promise for the future, with superposition and entanglement making quantum algorithms much more efficient and effective than their classical counterparts. Quantum ML aims to apply these quantum principles to the groundbreaking field of ML, such as for image classification. Convolutional Neural Networks (CNNs) are very effective for classification, so we study a Quantum Convolutional Neural Network (QCNN) that is trained to distinguish between handwritten numbers in the MNIST dataset and compared to a similar-sized classical network. After tuning the QCNN and quantum encoding, the QCNN achieved comparable accuracy to the classical network.