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International Conference on System Theory, Control and Computing (ICSTCC)

Image processing using quantum computing

Simona Caraiman, Vasile Manta

Published Jan 1, 2012• 48 citations
OpenAlex

Abstract

According to the principles of quantum physics, the computing power of a quantum machine is immense compared to that of a classical one. Encouraged by this idea important applications of quantum computation to computer science have been developed (e.g. faster algorithms, secure transmissions). In this paper we investigate the implications of using quantum computing systems in the field of image processing. We consider basic gray level transformations such as image negatives, binarization, histogram computation and histogram equalization and show how these operations can be expressed using the quantum formalism. Moreover, we show how the efficient exploitation of the special properties of quantum computation leads to better performance for the proposed quantum operations as compared to the classical correspondents.

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According to the principles of quantum physics, the computing power of a quantum machine is immense compared to that of a classical one. Encouraged by this idea important applications of quantum computation to computer science have been developed (e.g. faster algorithms, secure transmissions). In this paper we investigate the implications of using quantum computing systems in the field of image processing. We consider basic gray level transformations such as image negatives, binarization, histogram computation and histogram equalization and show how these operations can be expressed using the quantum formalism. Moreover, we show how the efficient exploitation of the special properties of quantum computation leads to better performance for the proposed quantum operations as compared to the classical correspondents.
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In this paper we address the emerging field of Quantum Image Processing. We investigate the use of quantum computing systems to represent and manipulate images. In particular, we consider the basic task of image filtering. We prove that a quantum version for this operation can be achieved, even though the quantum convolution of two sequences is physically impossible. In our approach we use the principle of the quantum oracle to implement the filter function. We provide the quantum circuit that implements the filtering task and present the results of several simulation experiments on grayscale images. There are important differences between the classical and the quantum implementations for image filtering. We analyze these differences and show that the major advantage of the quantum approach lies in the exploitation of the efficient implementation of the quantum Fourier transform.
Abstract Medical imaging is considered one of the most important areas within scientific imaging due to the rapid and ongoing development in computer‐aided medical image visualisation, advances in analysis approaches, and computer‐aided diagnosis. Here, the principles of quantum computation and information to develop the field of medical image processing will be reviewed. The advancement of quantum computation in image processing has proved its outstanding properties for processing and storage capacity compared to the classical methods. This review provides a comprehensive summary of the common advanced approaches, methodologies and advanced applications in medical images based on quantum computation.
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This article explores the use of quantum computing for image processing and inversion. The advantages of quantum data processing methods over classical methods are analyzed. A quantum circuit can solve the fundamental image processing task of image inversion. The application of quantum NOT gates to alter pixel states corresponding to grayscale image values was investigated. It has been demonstrated that using amplitude angles to encode pixel values enables the efficient creation of quantum superposition on an image. The solution involved generating a negative of the image using the X-gate and the Hadamard operation. A quantum circuit was designed and tested on a simulator; an algorithm was also proposed for converting measurement results into a finalized image. The study highlights the advantages and disadvantages of the quantum approach, the linearity of transformations, and scalability prospects. Quantum image processing algorithms can enhance performance when handling large datasets.
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.