The digitization of histopathology slides into high-resolution Whole-Slide Images (WSIs) has created a computational bottleneck for classical image processing algorithms. Tasks such as feature extraction and tumor segmentation require immense classical memory and processing power. This paper explores the application of Quantum Image Processing (QIP) as a solution. By utilizing frameworks such as the Flexible Representation of Quantum Images (FRQI) and the Novel Enhanced Quantum Representation (NEQR), we theoretically demonstrate how multi-gigapixel tissue scans can be encoded into high-dimensional Hilbert spaces. We propose a hybrid quantum-classical architecture that performs quantum edge detection via the Quantum Fourier Transform (QFT), achieving an exponential reduction in computational time complexity compared to classical convolutional edge filters. The results establish a foundational methodology for integrating quantum algorithms into clinical digital pathology workflows.The digitization of histopathology slides into high-resolution Whole-Slide Images (WSIs) has created a computational bottleneck for classical image processing algorithms. Tasks such as feature extraction and tumor segmentation require immense classical memory and processing power. This paper explores the application of Quantum Image Processing (QIP) as a solution. By utilizing frameworks such as the Flexible Representation of Quantum Images (FRQI) and the Novel Enhanced Quantum Representation (NEQR), we theoretically demonstrate how multi-gigapixel tissue scans can be encoded into high-dimensional Hilbert spaces. We propose a hybrid quantum-classical architecture that performs quantum edge detection via the Quantum Fourier Transform (QFT), achieving an exponential reduction in computational time complexity compared to classical convolutional edge filters. The results establish a foundational methodology for integrating quantum algorithms into clinical digital pathology workflows.

International Journal of Engineering Development and ResearchAug 1, 20260 citations

Hybrid Algorithms for Image Processing Leveraging Quantum Computing for Enhanced Performance

J. Ramprabu, S Prabhavathy

The integration of quantum computing into image processing represents a significant advancement in computational technology, offering transformative capabilities that enhance performance, efficiency, and accuracy. This chapter explores hybrid quantum-classical algorithms, focusing on how quantum subroutines can be integrated into classical image processing workflows. A comparative analysis of various quantum image encoding techniques highlights their strengths and limitations, paving the way for optimized applications across diverse fields. Key advantages of quantum-enhanced image processing, such as improved speed in high-resolution data analysis and superior optimization methods, are discussed in relation to real-world applications, including medical imaging and computer vision. The chapter anticipates future developments in quantum technology, emphasizing the need for interdisciplinary collaboration to unlock the full potential of quantum computing in digital image applications. By addressing the critical gaps in current methodologies, this work aims to inspire further research and innovation in the realm of quantum-enhanced image processing.The integration of quantum computing into image processing represents a significant advancement in computational technology, offering transformative capabilities that enhance performance, efficiency, and accuracy. This chapter explores hybrid quantum-classical algorithms, focusing on how quantum subroutines can be integrated into classical image processing workflows. A comparative analysis of various quantum image encoding techniques highlights their strengths and limitations, paving the way for optimized applications across diverse fields. Key advantages of quantum-enhanced image processing, such as improved speed in high-resolution data analysis and superior optimization methods, are discussed in relation to real-world applications, including medical imaging and computer vision. The chapter anticipates future developments in quantum technology, emphasizing the need for interdisciplinary collaboration to unlock the full potential of quantum computing in digital image applications. By addressing the critical gaps in current methodologies, this work aims to inspire further research and innovation in the realm of quantum-enhanced image processing.

Dec 7, 20240 citations

Quantum Image Processing and Its Applications

J. Jennifer Ranjani

Springer eBooksJan 1, 20190 citations

Quantum image dataset transform (QIDT) for image processing

Zorkan Erkan, Javad Rahebi, Aref Yelghi

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.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.

Quantum Information ProcessingMay 26, 20254 citations

\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.