Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

Applied Mathematics and Nonlinear Sciences

Quantum Information Technology Facilitates Innovative Research on Traditional Painting Art Styles

Jing Xu

Published Jan 1, 2024• 1 citation
Publisher

Abstract

Abstract The continuous progress of the network and big data has brought far-reaching influence and potential opportunities to traditional painting design, and at the same time, it continues to shape the content and form of its artistic style. This paper employs the quantum image enhancement algorithm to transform the traditional painting image into a quantum pure state. Subsequently, it segments each pixel to extract edge information, thereby constructing the traditional painting image state. Next, we employ the quantum image color enhancement algorithm to amplify the color aesthetics of the extracted image state, thereby fostering innovation. Finally, this paper proposes an innovative approach to traditional painting styles using quantum information technology. This paper proposes a quantum image enhancement algorithm that outperforms the classical program in all quality indicators after processing traditional painting images, thereby establishing a theoretical foundation for future research. The study found that traditional paintings made with the help of a quantum image enhancement algorithm have a higher quality of art style innovation and artistic value. The study also found that both types of works, plants (4.37 points) and myths (4.50 points), got higher scores on the tests. This paper brings a fresh perspective to the creation of traditional painting art, paving the way for a more intelligent and diverse future for this genre.

DOI
10.2478/amns-2024-2456

Related papers

Quantum Style Transfer for Creative Image Synthesis

V. Diana Earshia, S. Jyothirmaye, Barno Matchanova +3 more

Quantum computing is a new field for computer techniques that defy accepted wisdom in a variety of fields, including image processing and artificial intelligence. This study explores a new approach to creative image synthesis by combining the ideas of quantum physics with neural networks and style transfer techniques. In this work, we provide Quantum Style Transfer (QST), a hybrid methodology that combines the unique characteristics of quantum computing, such as superposition, entanglement, and quantum parallelism, with conventional deep learning techniques for style transfer. Traditional style transfer techniques can be computationally expensive and sometimes yield results that lack artistic delicacy and coherence. These techniques change the content of one image to fit the aesthetic of another. Through the use of quantum computing, we enhance the expressiveness and adaptability of style transfer, enabling the creation of highly creative and unique images that go beyond conventional methods. By employing quantum states to encode visual data, we propose a quantum-enhanced neural network design that optimizes the transformation process by creating a richer, multidimensional space for style blending. This new framework can provide outputs that are more complex, varied, and aesthetically beautiful than traditional deep learning models, which presents intriguing possibilities for artistic and design expression. Additionally, by reducing the processing load involved in training large neural networks, the quantum approach may speed up and improve the creation of creative pictures.

International Conference on Innovative Data Communication Technologies and Application (ICIDCA)•Oct 6, 2025•0 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.

•Dec 7, 2024•0 citations

A brief survey on image segmentation based on quantum inspired neural network

Václav Snåšel, Jan Platoš, Pankaj Kumar Pal +1 more

Information retrieval is a rudimentary approach which is highly solicited for image analysis research. In classical image analysis, image processing recovers the objects from the noisy images in an efficient way at the cost of substantial time complexity. Quantum computation assisted algorithms play a vital role to extract objects from binary, grey, pure or from true colour images using quantum computing principles which reduces the time complexity while improving the segmentation efficiency. These algorithms take recourse to the quantum superposition principle in achieving the task. A neuro-biological network architecture comprises of different nodes corresponding to the image pixels which are converted to the equivalent qubit neurons in the quantum inspired versions. The ability of information retrieval capability from the images by means of qubit neurons is a much-talked affair now-a-day. In this paper, the authors attempt to present a brief survey on image segmentation techniques using the state-of-the-art techniques including quantum inspired neural networks.

International Journal of Hybrid Intelligence•Jan 1, 2023•1 citation

Implementation of Classic Image Transformation Algorithm to Quantum State, Boundary Extraction and Transformation of Half-Tone Image to Binary

Sergey M. Gushanskiy, Viktor Potapov, Omar Correa Madrigal

Abstract The aim of the research is computer simulation of a quantum algorithm to solve the problem of transforming a classical image using quantum computing tools and methods, studying recognition algorithms and creating a recognition model using quantum methods. The method of quantum modeling makes it possible to convert a classical image into a quantum state, select boundaries and convert a grayscale image to a binary one, and shows the possibilities of the quantum information theory in interpreting classical problems. The main results of the article are the developed quantum algorithm that allows recognizing objects, as well as the quantum method aimed at representing/processing a color pixel image. The scientific novelty of the article is expressed in the construction of a quantum system, an exponential increase in the speed of solving computational NP-complete problems, which on classical machines can be solved in unacceptable time. The motivation for writing the work was a high growth interest in quantum computing and the benefits that they guarantee. The development of the theoretical foundations of creating software systems and the design of algorithms for new information technologies and specialized computing systems is a dynamic field, as evidenced by the number of existing works in this direction. The developed algorithms for various problems of complexity classes can give a significant gain in efficiency in comparison with existing classical ones and provide a solution to a number of complex mathematical (including cryptographic) problems.

Cybernetics and Information Technologies•Jun 1, 2020•3 citations

A brief survey on image segmentation based on quantum inspired neural network

Pankaj Kumar Pal, Siddhartha Bhattacharyya, Jan Platoš +1 more

Information retrieval is a rudimentary approach which is highly solicited for image analysis research. In classical image analysis, image processing recovers the objects from the noisy images in an efficient way at the cost of substantial time complexity. Quantum computation assisted algorithms play a vital role to extract objects from binary, grey, pure or from true colour images using quantum computing principles which reduces the time complexity while improving the segmentation efficiency. These algorithms take recourse to the quantum superposition principle in achieving the task. A neuro-biological network architecture comprises of different nodes corresponding to the image pixels which are converted to the equivalent qubit neurons in the quantum inspired versions. The ability of information retrieval capability from the images by means of qubit neurons is a much-talked affair now-a-day. In this paper, the authors attempt to present a brief survey on image segmentation techniques using the state-of-the-art techniques including quantum inspired neural networks.

International Journal of Hybrid Intelligence•Jan 1, 2023•2 citations

Quantum Algorithms for Image Processing: Enhancing Computational Efficiency and Accuracy in High-Dimensional Visual Data Analysis

Ata Ulaş Güler

Quantum algorithms have emerged as a transformative approach to address the computational challenges of high-dimensional image processing tasks. This research investigates the integration of quantum computing principles, such as Quantum Fourier Transform (QFT) and Quantum Principal Component Analysis (QPCA), to optimize processes like edge detection, noise reduction, and image segmentation. These quantum-enhanced techniques promise significant improvements in computational efficiency and accuracy compared to classical methods, especially when handling complex visual data in real-time applications like medical imaging, satellite imagery, and autonomous systems. Additionally, the study explores hybrid quantum-classical architectures to bridge current limitations in noisy intermediate-scale quantum (NISQ) devices, ensuring resource optimization and scalability. The potential of quantum algorithms to provide faster, more precise solutions is evaluated alongside challenges like error mitigation and algorithmic stability. This research highlights the future role of quantum computing in revolutionizing image processing, paving the way for breakthroughs in computer vision and artificial intelligence.

Human computer interaction.•Nov 27, 2024•1 citation
Abstract The continuous progress of the network and big data has brought far-reaching influence and potential opportunities to traditional painting design, and at the same time, it continues to shape the content and form of its artistic style. This paper employs the quantum image enhancement algorithm to transform the traditional painting image into a quantum pure state. Subsequently, it segments each pixel to extract edge information, thereby constructing the traditional painting image state. Next, we employ the quantum image color enhancement algorithm to amplify the color aesthetics of the extracted image state, thereby fostering innovation. Finally, this paper proposes an innovative approach to traditional painting styles using quantum information technology. This paper proposes a quantum image enhancement algorithm that outperforms the classical program in all quality indicators after processing traditional painting images, thereby establishing a theoretical foundation for future research. The study found that traditional paintings made with the help of a quantum image enhancement algorithm have a higher quality of art style innovation and artistic value. The study also found that both types of works, plants (4.37 points) and myths (4.50 points), got higher scores on the tests. This paper brings a fresh perspective to the creation of traditional painting art, paving the way for a more intelligent and diverse future for this genre.
Quantum computing is a new field for computer techniques that defy accepted wisdom in a variety of fields, including image processing and artificial intelligence. This study explores a new approach to creative image synthesis by combining the ideas of quantum physics with neural networks and style transfer techniques. In this work, we provide Quantum Style Transfer (QST), a hybrid methodology that combines the unique characteristics of quantum computing, such as superposition, entanglement, and quantum parallelism, with conventional deep learning techniques for style transfer. Traditional style transfer techniques can be computationally expensive and sometimes yield results that lack artistic delicacy and coherence. These techniques change the content of one image to fit the aesthetic of another. Through the use of quantum computing, we enhance the expressiveness and adaptability of style transfer, enabling the creation of highly creative and unique images that go beyond conventional methods. By employing quantum states to encode visual data, we propose a quantum-enhanced neural network design that optimizes the transformation process by creating a richer, multidimensional space for style blending. This new framework can provide outputs that are more complex, varied, and aesthetically beautiful than traditional deep learning models, which presents intriguing possibilities for artistic and design expression. Additionally, by reducing the processing load involved in training large neural networks, the quantum approach may speed up and improve the creation of creative pictures.
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.
Information retrieval is a rudimentary approach which is highly solicited for image analysis research. In classical image analysis, image processing recovers the objects from the noisy images in an efficient way at the cost of substantial time complexity. Quantum computation assisted algorithms play a vital role to extract objects from binary, grey, pure or from true colour images using quantum computing principles which reduces the time complexity while improving the segmentation efficiency. These algorithms take recourse to the quantum superposition principle in achieving the task. A neuro-biological network architecture comprises of different nodes corresponding to the image pixels which are converted to the equivalent qubit neurons in the quantum inspired versions. The ability of information retrieval capability from the images by means of qubit neurons is a much-talked affair now-a-day. In this paper, the authors attempt to present a brief survey on image segmentation techniques using the state-of-the-art techniques including quantum inspired neural networks.
Abstract The aim of the research is computer simulation of a quantum algorithm to solve the problem of transforming a classical image using quantum computing tools and methods, studying recognition algorithms and creating a recognition model using quantum methods. The method of quantum modeling makes it possible to convert a classical image into a quantum state, select boundaries and convert a grayscale image to a binary one, and shows the possibilities of the quantum information theory in interpreting classical problems. The main results of the article are the developed quantum algorithm that allows recognizing objects, as well as the quantum method aimed at representing/processing a color pixel image. The scientific novelty of the article is expressed in the construction of a quantum system, an exponential increase in the speed of solving computational NP-complete problems, which on classical machines can be solved in unacceptable time. The motivation for writing the work was a high growth interest in quantum computing and the benefits that they guarantee. The development of the theoretical foundations of creating software systems and the design of algorithms for new information technologies and specialized computing systems is a dynamic field, as evidenced by the number of existing works in this direction. The developed algorithms for various problems of complexity classes can give a significant gain in efficiency in comparison with existing classical ones and provide a solution to a number of complex mathematical (including cryptographic) problems.
Information retrieval is a rudimentary approach which is highly solicited for image analysis research. In classical image analysis, image processing recovers the objects from the noisy images in an efficient way at the cost of substantial time complexity. Quantum computation assisted algorithms play a vital role to extract objects from binary, grey, pure or from true colour images using quantum computing principles which reduces the time complexity while improving the segmentation efficiency. These algorithms take recourse to the quantum superposition principle in achieving the task. A neuro-biological network architecture comprises of different nodes corresponding to the image pixels which are converted to the equivalent qubit neurons in the quantum inspired versions. The ability of information retrieval capability from the images by means of qubit neurons is a much-talked affair now-a-day. In this paper, the authors attempt to present a brief survey on image segmentation techniques using the state-of-the-art techniques including quantum inspired neural networks.
Quantum algorithms have emerged as a transformative approach to address the computational challenges of high-dimensional image processing tasks. This research investigates the integration of quantum computing principles, such as Quantum Fourier Transform (QFT) and Quantum Principal Component Analysis (QPCA), to optimize processes like edge detection, noise reduction, and image segmentation. These quantum-enhanced techniques promise significant improvements in computational efficiency and accuracy compared to classical methods, especially when handling complex visual data in real-time applications like medical imaging, satellite imagery, and autonomous systems. Additionally, the study explores hybrid quantum-classical architectures to bridge current limitations in noisy intermediate-scale quantum (NISQ) devices, ensuring resource optimization and scalability. The potential of quantum algorithms to provide faster, more precise solutions is evaluated alongside challenges like error mitigation and algorithmic stability. This research highlights the future role of quantum computing in revolutionizing image processing, paving the way for breakthroughs in computer vision and artificial intelligence.