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Quantum Information Processing

Quantum image dataset transform (QIDT) for image processing

Zorkan Erkan, Javad Rahebi, Aref Yelghi

Published May 26, 2025• 4 citations
Publisher

Abstract

Abstract Existing CNN architectures have been developed to train digital image datasets obtained from hardware systems operating with classical bits, such as optical cameras. With the increase of quantum computing algorithms and quantum system providers, academic research is being conducted to combine the strengths of classical computing and quantum algorithms. This fusion allows for the development of hybrid quantum systems, with proposed methods specifically for the quantum representation of digital images. While methods for transforming digital images into quantum-compatible circuits have been proposed, no study has been found on the quantum transformation of entire datasets, especially for the use of fully classical CNN architectures. This article presents the quantum image dataset transform method, which utilizes quantum circuits to transform digital images and create a new dataset of the transformed images. Each of the 10,000 digital images of 28 $$\times $$ × 28 dimensions in the MNIST handwritten digits dataset is individually sub-parts, and the common weight values of each segment are determined as the phase value to be used in the quantum circuit. The quantum outputs of each sub-part are converted into classical equivalents by creating a quantum converter, and a new digital image is obtained by combining all the sub-parts. The newly generated digital images are labeled as $${\textbf {MNIST}} {\textbf {Q}}^{{\textbf {+}}}_{{{\textbf {image}}}}$$ MNIST Q image + and are publicly shared along with the original MNIST dataset. The paper evaluates both a custom 3-layer CNN architecture and several pre-trained models, including EfficientNetV2B3, ResNet-50, DenseNet-121, and ConvNeXt Tiny. After training for 30 epochs, the 3-layer CNN architecture achieved the highest accuracy of 99.23%, significantly outperforming the pre-trained models, with DenseNet-121 achieving 81.70%, EfficientNetV2B3 64.23%, ResNet-50 53.25%, and ConvNeXt Tiny 53.41%. The results highlight the superior performance of the 3-layer CNN in adapting to the quantum-transformed dataset and demonstrate the potential of quantum transformations to enhance the learning ability of classical CNN models. This foundational research aims to pave the way for further exploration into the integration of quantum-transformed datasets in classical deep learning frameworks.

DOI
10.1007/s11128-025-04754-1

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Abstract Existing CNN architectures have been developed to train digital image datasets obtained from hardware systems operating with classical bits, such as optical cameras. With the increase of quantum computing algorithms and quantum system providers, academic research is being conducted to combine the strengths of classical computing and quantum algorithms. This fusion allows for the development of hybrid quantum systems, with proposed methods specifically for the quantum representation of digital images. While methods for transforming digital images into quantum-compatible circuits have been proposed, no study has been found on the quantum transformation of entire datasets, especially for the use of fully classical CNN architectures. This article presents the quantum image dataset transform method, which utilizes quantum circuits to transform digital images and create a new dataset of the transformed images. Each of the 10,000 digital images of 28 ×\times × × 28 dimensions in the MNIST handwritten digits dataset is individually sub-parts, and the common weight values of each segment are determined as the phase value to be used in the quantum circuit. The quantum outputs of each sub-part are converted into classical equivalents by creating a quantum converter, and a new digital image is obtained by combining all the sub-parts. The newly generated digital images are labeled as MNISTQimage+{\textbf {MNIST}} {\textbf {Q}}^{{\textbf {+}}}_{{{\textbf {image}}}}MNISTQimage+​ MNIST Q image + and are publicly shared along with the original MNIST dataset. The paper evaluates both a custom 3-layer CNN architecture and several pre-trained models, including EfficientNetV2B3, ResNet-50, DenseNet-121, and ConvNeXt Tiny. After training for 30 epochs, the 3-layer CNN architecture achieved the highest accuracy of 99.23%, significantly outperforming the pre-trained models, with DenseNet-121 achieving 81.70%, EfficientNetV2B3 64.23%, ResNet-50 53.25%, and ConvNeXt Tiny 53.41%. The results highlight the superior performance of the 3-layer CNN in adapting to the quantum-transformed dataset and demonstrate the potential of quantum transformations to enhance the learning ability of classical CNN models. This foundational research aims to pave the way for further exploration into the integration of quantum-transformed datasets in classical deep learning frameworks.
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In the field of image processing, a well-known model is the Convolutional Neural Network, or CNN. The unique benefit that sets this model apart is its exceptional ability to use the correlation information included in the data. Even with their amazing accomplishment, conventional CNNs could have trouble improving further in terms of generalization, accuracy, and computing economy. However, it could be challenging to train CNN correctly and process information quickly if the model or data dimensions are too large. This is since it will cause the data processing to lag. The Quantum Convolutional Neural Network, or QCNN for short, is a novel proposed quantum solution that might either enhance the functionality of an existing learning model or solve a problem requiring the combination of quantum computing with CNN. To highlight the flexibility and versatility of quantum circuits in improving feature extraction capabilities, this paper compares deep quantum circuit architecture designed for image-based tasks with classical Convolutional Neural Networks (CNNs) and a novel quantum circuit architecture. The covidx-cxr4 dataset was used to train quantum-CNN models, and their results were compared against those of other models. The results show that when paired with innovative feature extraction methods, the suggested deep Quantum Convolutional Neural Network (QCNN) outperformed the conventional CNN in terms of processing speed and recognition accuracy. Even though it required more processing time, QCNN outperformed CNN in terms of recognition accuracy. When training on the covidx-cxr4 dataset, this dominance becomes much more apparent, demonstrating how deeper quantum computing has the potential to completely transform image classification problems.
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.
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