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Internet Technology Letters

Optimization of Quantum Dilated Convolutional Neural Networks: Image Recognition With Quantum Computing

S. Rahamat Basha, Pankaj Pathak, M. Sudha, K. V. Soumya, J. Arockia Venice

Published Apr 21, 2025• 12 citations
Publisher

Abstract

ABSTRACT As computer vision tasks increasingly rely on Convolutional Neural Networks (CNNs) with ever‐expanding parameter counts, the need for computational resources for model training is growing unsustainable, surpassing traditional computing hardware's progress. To address this challenge, emerging paradigms such as quantum computing are gaining attention as prospective alternatives for the future. This manuscript proposes Quantum Dilated Convolutional Neural Networks Revolutionizing Image Recognition with Quantum Computing (QDCNN‐IR‐QC). The first step is to use the MNIST dataset for the input pictures. Subsequently, Improved Bilateral Texture Filtering (IBTF) is used to preprocess the input pictures. Subsequently, E‐LBP is used to extract pertinent features from the preprocessed pictures. In most cases, E‐LBP does not show that optimization methods for picture recognition have been adjusted. Therefore, in order to adjust the E‐LBP weight parameter, this paper suggests an ISMO optimization approach. Lastly, a new quantum architecture for picture identification is developed using QDCNN. To implement the proposed approach, Python is used. This is where metrics like F‐Measure, accuracy, sensitivity, specificity, and precision are assessed. When compared to current techniques such as QOCNN‐IR‐QC, ANN‐IR‐QC, and QKNN‐IR‐QC, the proposed approaches provide 5.27%, 7.21%, and 8.23% greater accuracy, respectively, in terms of efficiency.

DOI
10.1002/itl2.70027

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ABSTRACT As computer vision tasks increasingly rely on Convolutional Neural Networks (CNNs) with ever‐expanding parameter counts, the need for computational resources for model training is growing unsustainable, surpassing traditional computing hardware's progress. To address this challenge, emerging paradigms such as quantum computing are gaining attention as prospective alternatives for the future. This manuscript proposes Quantum Dilated Convolutional Neural Networks Revolutionizing Image Recognition with Quantum Computing (QDCNN‐IR‐QC). The first step is to use the MNIST dataset for the input pictures. Subsequently, Improved Bilateral Texture Filtering (IBTF) is used to preprocess the input pictures. Subsequently, E‐LBP is used to extract pertinent features from the preprocessed pictures. In most cases, E‐LBP does not show that optimization methods for picture recognition have been adjusted. Therefore, in order to adjust the E‐LBP weight parameter, this paper suggests an ISMO optimization approach. Lastly, a new quantum architecture for picture identification is developed using QDCNN. To implement the proposed approach, Python is used. This is where metrics like F‐Measure, accuracy, sensitivity, specificity, and precision are assessed. When compared to current techniques such as QOCNN‐IR‐QC, ANN‐IR‐QC, and QKNN‐IR‐QC, the proposed approaches provide 5.27%, 7.21%, and 8.23% greater accuracy, respectively, in terms of efficiency.
Large machine learning models based on Convolutional Neural Networks (CNNs) with rapidly increasing numbers of parameters are being deployed in a wide array of computer vision tasks. However, the insatiable demand for computing resources required to train these models is fast outpacing the advancement of classical computing hardware, and new frameworks including Optical Neural Networks (ONNs) and quantum computing are being explored as future alternatives. In this work, we report a novel quantum computing based deep learning model, the Quantum Optical Convolutional Neural Network (QOCNN), to alleviate the computational bottleneck in future computer vision applications. Using the MNIST dataset, we have benchmarked this new architecture against a traditional CNN model based on the seminal LeNet architecture. We have also compared the performance against previously reported ONNs, the GridNet and ComplexNet, and a Quantum Optical Neural Network (QONN) that we built by combining the ComplexNet with quantum-inspired sinusoidal nonlinearities. Our work extends the prior research on QONN by adding quantum convolution and pooling layers. We have evaluated the models using the metrics of accuracies, confusion matrices, Receiver Operating Characteristic curves, and Matthews Correlation Coefficients. The performance of the models were similar, and the metrics indicated that the new QOCNN model is robust. Finally, we estimated the gains in computational efficiencies from executing this novel framework on a quantum computer, concluding that switching to a quantum computing based approach to deep learning may result in comparable accuracies to classical models while simultaneously achieving unprecedented boosts in computational performances and power allocation.
In recent years, with rapid progress in the development of quantum technologies, quantum machine learning has attracted a lot of interest. In particular, a family of hybrid quantum-classical neural networks, consisting of classical and quantum elements, has been massively explored for the purpose of improving the performance of classical neural networks. In this paper, we propose a novel hybrid quantum-classical algorithm called quantum dilated convolutional neural networks (QDCNNs). Our method extends the concept of dilated convolution, which has been widely applied in modern deep learning algorithms, to the context of hybrid neural networks. The proposed QDCNNs are able to capture larger context during the quantum convolution process while reducing the computational cost. We perform empirical experiments on MNIST and Fashion-MNIST datasets for the task of image recognition and demonstrate that QDCNN models generally enjoy better performances in terms of both accuracy and computation efficiency compared to existing quantum convolutional neural networks (QCNNs).
In recent years, with rapid progress in the development of quantum technologies, quantum machine learning has attracted a lot of interest. In particular, a family of hybrid quantum-classical neural networks, consisting of classical and quantum elements, has been massively explored for the purpose of improving the performance of classical neural networks. In this paper, we propose a novel hybrid quantum-classical algorithm called quantum dilated convolutional neural networks (QDCNNs). Our method extends the concept of dilated convolution, which has been widely applied in modern deep learning algorithms, to the context of hybrid neural networks. The proposed QDCNNs are able to capture larger context during the quantum convolution process while reducing the computational cost. We perform empirical experiments on MNIST and Fashion-MNIST datasets for the task of image recognition and demonstrate that QDCNN models generally enjoy better performances in terms of both accuracy and computation efficiency compared to existing quantum convolutional neural networks (QCNNs).
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.
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, including70 K28×2870 ~\mathrm{K} 28 \times 2870 K28×28grayscale images from MNIST and FashionMNIST, and60K\mathbf{6 0 K}60Kdownsampled 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.
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.