While machine learning technology evolves fast with the development of computing power and new algorithms, integrating theories of quantum mechanics gives novel opportunities to it. This paper focuses on the comparative analysis of Quantum Convolutional Neural Networks (QCNN) and traditional Convolutional Neural Networks (CNN), which are important techniques in the domain of machine learning, especially image processing. By doing a literature review and analysis, the comparison includes performance differences, application potential, and inherent challenges of QCNN. Although QCNN is comparably novel, it does exhibit superiority over CNN in specific cases theoretically like high dimensional and complex datasets. QCNN currently is facing problems due to hardware constraints and immaturity in quantum computing and doesn't show superior performance when running on actual quantum machines. With the development of quantum computing and hardware, QCNN has a strong potential for use in machine learning. The research compares the current performance of QCNN and CNN to make suggestions about applying them in real cases and guide the path for future research.
While machine learning technology evolves fast with the development of computing power and new algorithms, integrating theories of quantum mechanics gives novel opportunities to it. This paper focuses on the comparative analysis of Quantum Convolutional Neural Networks (QCNN) and traditional Convolutional Neural Networks (CNN), which are important techniques in the domain of machine learning, especially image processing. By doing a literature review and analysis, the comparison includes performance differences, application potential, and inherent challenges of QCNN. Although QCNN is comparably novel, it does exhibit superiority over CNN in specific cases theoretically like high dimensional and complex datasets. QCNN currently is facing problems due to hardware constraints and immaturity in quantum computing and doesn't show superior performance when running on actual quantum machines. With the development of quantum computing and hardware, QCNN has a strong potential for use in machine learning. The research compares the current performance of QCNN and CNN to make suggestions about applying them in real cases and guide the path for future research.
Quantum convolutional neural networks (QCNNs) are a highly appealing architecture that combines quantum computing and deep learning. Inspired by classical convolutional neural network (CNN) hierarchical feature extraction, QCNNs use quantum operations such as entanglement, superposition, and measurement to capture complex correlations in high-dimensional datasets. Since their introduction, QCNNs have evolved into several architectural variants, including fully quantum, variational, hybrid, and graph-based models, and have been investigated in applications ranging from quantum many-body physics to classical machine learning tasks such as image classification, speech recognition, and time-series forecasting. The development of software ecosystems such as Qiskit Machine Learning, Pennylane, and TensorFlow Quantum (TFQ) has facilitated rapid development and experimentation, speeding up research. Despite these developments, existing research remains divided, with many focused on individual implementations and lacking a unifying taxonomy or full review. This article addresses that gap by providing a systematic and holistic survey on QCNN, offering comparative insights across key architectures, applications, and toolboxes, and outlining open challenges. We also identify potential study areas, such as scalable architectures, domain-knowledge integration, fault tolerance, and security. This survey seeks to serve as a basic reference for furthering QCNN research in the current near-term quantum era and beyond, providing both architectural insights and application-driven viewpoints.
Convolutional Neural Networks (CNNs) are extensively utilized for image classification due to their ability to exploit data correlations effectively. However, traditional CNNs encounter several challenges, including limitations in accuracy improvement, computational efficiency, explain ability, and generalization. As data dimensionality or model complexity increases, CNNs often become more difficult to train efficiently, leading to slower processing times. To overcome these challenges, Quantum Convolutional Neural Networks (QCNNs) offer a promising quantum-based approach to enhance existing models, particularly by improving training efficiency and reducing processing times. This paper provides a comparative analysis of classical CNNs and a novel quantum circuit architecture tailored for image-based tasks. The analysis emphasizes the adaptability and potential of quantum circuits to enhance feature extraction, recognition accuracy, and processing efficiency.We applied various QCNN architectures to the Chinese-MNIST dataset and compared their performance with traditional CNN models. Although QCNNs achieved slightly lower accuracy, they demonstrated significantly greater stability. These findings high- light QCNNs’ potential to overcome the computational limitations of conventional deep learning, offering a promising pathway for quantum-enhanced solutions in computer vision. Future work will focus on refining quantum circuit designs, developing hybrid quantum-classical models, and extending QCNN applications to larger and more diverse datasets.
Quantum Convolutional Neural Networks (QCNNs) have emerged as one of the most promising architectures in Quantum Machine Learning (QML), enabling hierarchical quantum feature extraction and offering potential advantages over classical CNNs in expressivity and scalability. This study presents a Systematic Literature Review (SLR) on QCNN development from 2019 to 2025, covering theoretical foundations, model architectures, noise resilience, benchmark performance, and applications in materials informatics, chemistry, image recognition, quantum phase classification, and cybersecurity. The SLR followed PRISMA guidelines, screening 214 publications and selecting 47 primary studies. The review finds that QCNNs consistently outperform classical baselines in small-data and high-dimensional regimes due to quantum feature maps and entanglement-driven locality. Significant limitations include noise sensitivity, limited qubit availability, and a lack of standardized datasets for benchmarking. The novelty of this work lies in providing the first comprehensive synthesis of QCNN research across theory, simulations, and real-hardware deployment, offering a roadmap for research gaps and future directions. The findings confirm that QCNNs are strong candidates for NISQ-era applications, especially in physics-informed learning.
This paper presents a comprehensive evaluation of the potential of Quantum Convolutional Neural Networks (QCNNs) in comparison to classical Convolutional Neural Networks (CNNs) and Artificial / Classical Neural Network (ANN) models. With the increasing amount of data, utilizing computing methods like CNN in real-time has become challenging. QCNNs overcome this challenge by utilizing qubits to represent data in a quantum environment and applying CNN structures to quantum computers. The time and accuracy of QCNNs are compared with classical CNNs and ANN models under different conditions such as batch size and input size. The maximum complexity level that QCNNs can handle in terms of these parameters is also investigated. The analysis shows that QCNNs have the potential to outperform both classical CNNs and ANN models in terms of accuracy and efficiency for certain applications, demonstrating their promise as a powerful tool in the field of machine learning.
Deep learning algorithms and models have made an impact in the area of AI and machine learning, one among them is CNN. CNN is extensively used in the area of image recognition and object detection for classification purposes. CNN is composed of several layers of filters to get feature maps of input data, yet foremost and crucial one is convolutional layer, hence the name Convolutional neural networks. However, the growth of quantum computing and quantum neural network in deep learning is limited. Three main obstacles that limit the growth of these are, first is due to the lack of real-time quantum computers to experiment with. Second is the improper training algorithms and at last, non-linearity nature of the neural networks. This paper introduces a novel approach to begin one's journey in quantum computing, along with solutions and developments. This work provides a detailed description of architectures, frameworks and algorithms used for implementing a QCNN model. The research was made regarding image recognition and object detection using QCNN and found that QCNN can increase the computational speeds with better performance metrics compared to classical computational methods. This paper also debates about applications of QCNN in computer vision, signal and image processing, Pharmaceuticals, Cryptography and various other fields. This study also explains Key players and future work in developing quantum computers, quantum computing algorithms, software and hardware support to implement QCNN in various applications.
A common model for classifying images is the convolutional neural network (CNN), which has the benefit of effectively using data correlation information. Despite their remarkable success, classical CNNs may face challenges in achieving further improvements in accuracy, computational efficiency, explainability, and generalization. However, if the specified data dimension or model grows too large, CNN becomes difficult to train effectively with a slowdown processing. In order to address a problem using CNN utilizing quantum computing, Quantum Convolutional Neural Network (QCNN) proposes a novel quantum solution or enhances the functionality of an existing learning model in terms of processing time during training. This paper presents a comparative analysis between classical Convolutional Neural Networks (CNNs) and a novel quantum circuit architecture tailored for image-based tasks, emphasizing the adaptability and versatility of quantum circuits in enhancing feature extraction capabilities and then final accuracy and processing time. A MNIST and covidx-cxr3 datasets was used to train quantum-CNN models, and the results of these comparisons were made with traditional CNN performance. The results demonstrate that the suggested QCNN beat the traditional CNN in terms of recognition accuracy and processing speed (process time) when combined with cutting-edge feature extraction techniques. This superiority is particularly evident when trained on the covidx-cxr3 dataset, highlighting the potential for quantum computing to revolutionize image classification tasks.