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 \({\textbf {MNIST}} {\textbf {Q}}^{{\textbf {+}}}_{{{\textbf {image}}}}\) 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.
