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CRC Press eBooks

Quantum Image Analysis – Status and Perspectives

Konstantinos Tziridis, Τheofanis Kalampokas, George A. Papakostas

Published Mar 30, 2023• 1 citation
Publisher

Abstract

Nowadays, image analysis constitutes a key function of modern smart devices and computer applications. In recent years, image analysis has made significant progress due to the availability of a large volume of data as well as due to the development and promotion of deep learning. GPU graphics cards, as well as big data analysis frameworks (e.g. MapReduce and Spark), mainly meets the computing needs of modern image analysis applications. In this context, recently, quantum computing has emerged as an alternative that can meet the increasing computing needs. This chapter explores the degree of penetration of quantum computing into the field of image analysis, through the recording, editing, and presentation of publications made by the scientific community. The purpose of this chapter is not only to capture the current situation in the field of quantum image analysis (QIA) but also to raise open issues that pose challenges for further research in this research field. This chapter will be a useful guide to the early-stage researchers working in the field of quantum computing, providing the information gathered in a comprehensive and structured way.

DOI
10.1201/9781003053262-6

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Conf. Comput. Inf. Technol. (CIT), 2016, pp. 112-115. [13] A. Pandey and V. Ramesh, "Quantum computing for big data analysis," Indian J. Sci., vol. 14, no. 43, pp. 98-104, 2015. [14] J. Singh and M. Singh, "Evolution in quantum computing," in Proc. Int. Conf. Syst. Model. Adv. Res. Trends (SMART), 2016, pp. 267-270. [15] C. P. Chen and C. Y. Zhang, "Data-intensive applications, challenges, techniques and technologies: A survey on big data," Inf. Sci., vol. 275, pp. 314-347, 2014. [16] X. D. Cai et al., "Entanglement-based machine learning on a quantum computer," Phys. Rev. Lett., vol. 114, no. 11, p. 110504, 2015. [17] D. P. Acharjya and K. Ahmed, "A survey on big data analytics: Challenges, open research issues and tools," Int. J. Adv. Comput. Sci. Appl., vol. 7, no. 2, pp. 511-518, 2016. [18] S. Yu, "Big privacy: Challenges and opportunities of privacy study in the age of big data," IEEE Access, vol. 4, pp. 2751-2763, 2016. [19] O. N. Garcia and G. Bajwa, "Integration of quantum information systems in computer science and in electrical engineering," J. Integr. Des. Process Sci., vol. 19, no. 4, pp. 9-20, 2016. [20] A. JavadiAbhari et al., "ScaffCC: A framework for compilation and analysis of quantum computing programs," in Proc. 11th ACM Conf. Comput. Front., 2014, pp. 1-10. [21] S. Khanra, A. Dhir, A. N. Islam, and M. Mäntymäki, "Big data analytics in healthcare: A systematic literature review," Enterp. Inf. Syst., vol. 14, no. 7, pp. 878-912, 2020. [22] S. Ahmed, I. Ahmed, M. Kamruzzaman, and R. Saha, "Cybersecurity challenges in IT infrastructure and data management: A comprehensive review of threats, mitigation strategies, and future trend," Glob. Mainstream J. Innov. Eng. Emerg. Technol., vol. 1, no. 1, pp. 36-61, 2022. [23] O. M. Araz, T. M. Choi, D. L. Olson, and F. S. Salman, "Role of analytics for operational risk management in the era of big data," Decis. Sci., vol. 51, no. 6, pp. 1320-1346, 2020. [24] R. Orús, S. 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Journal of Engineering and Computational Intelligence Review•Jun 30, 2024•0 citations
Nowadays, image analysis constitutes a key function of modern smart devices and computer applications. In recent years, image analysis has made significant progress due to the availability of a large volume of data as well as due to the development and promotion of deep learning. GPU graphics cards, as well as big data analysis frameworks (e.g. MapReduce and Spark), mainly meets the computing needs of modern image analysis applications. In this context, recently, quantum computing has emerged as an alternative that can meet the increasing computing needs. This chapter explores the degree of penetration of quantum computing into the field of image analysis, through the recording, editing, and presentation of publications made by the scientific community. The purpose of this chapter is not only to capture the current situation in the field of quantum image analysis (QIA) but also to raise open issues that pose challenges for further research in this research field. This chapter will be a useful guide to the early-stage researchers working in the field of quantum computing, providing the information gathered in a comprehensive and structured way.
Abstract Image segmentation remains one of the most extensively studied problems in computer vision, with applications ranging from medical image analysis and autonomous driving to remote sensing and industrial inspection. While deep learning approaches currently dominate the field, interest in quantum computing has motivated the development of quantum and quantum-inspired segmentation techniques that explore alternative strategies for image representation, optimization, and learning. This survey provides a comprehensive review of quantum and quantum-inspired image segmentation methods within the broader context of modern segmentation research. To establish this context, we first examine the evolution of image segmentation methodologies, covering classical image processing techniques, machine learning approaches, and contemporary deep learning architectures.We then organize the existing quantum image segmentation literature into four categories: quantum-inspired, tensor network-based, hybrid quantum-classical, and fully quantum methods. Across these categories, we analyze computational principles, image encoding and representation strategies, application domains, and datasets. We further compare quantum and quantum-inspired approaches with established segmentation paradigms in terms of supervision, scalability, and hardware requirements. Our analysis indicates that quantum-inspired methods currently represent the most developed direction, while hybrid approaches are emerging and fully quantum methods remain constrained by quantum hardware, data encoding costs, and scalability challenges. By connecting quantum image segmentation with the broader evolution of segmentation research, this survey provides a unified perspective on the field. It identifies current trends, open challenges, and promising future directions for researchers exploring the emerging role of quantum computing in image segmentation.
This study provides a comprehensive review of the advancements, challenges, and potential implications of quantum computing in the field of big data analytics. The primary objective is to assess how quantum computing paradigms are transforming data processing and analysis, with a focus on their application across various sectors, including healthcare, finance, and scientific research. Employing a systematic literature review and content analysis, the study analyzes peer-reviewed articles, conference proceedings, and academic journals from databases such as PubMed, IEEE Xplore, and ScienceDirect. Key findings reveal that quantum computing, with its advanced algorithms and machine learning techniques, offers significant improvements in computational speed and efficiency over classical computing methods. This technological advancement enables the handling of large and complex datasets, presenting new opportunities in data analytics. However, the study also identifies challenges such as scalability, error correction, and integration with existing systems, which currently limit the full potential of quantum computing in big data analytics. The study concludes with strategic recommendations for industry leaders and policymakers, emphasizing the need for investment in research and development, the establishment of regulatory frameworks, and the development of educational programs to support this emerging field. Future research directions are suggested, focusing on overcoming technological limitations and exploring the long-term implications of quantum computing in various industries. This study contributes valuable insights into the evolving landscape of quantum computing and its significant impact on big data analytics. Keywords: Quantum Computing, Big Data Analytics, Advanced Algorithms, Data Processing.
This survey report presents an overview of the field of quantum computing, focusing on the application of quantum technologies in machine learning, deep learning, image processing, and other related areas. A comprehensive analysis of research papers published between 2005 and 2022 is conducted, covering the latest advancements in quantum-based techniques. The report provides insights into various types of quantum technologies, including quantum machine learning, deep learning, computing, learning, and image processing. The survey encompasses a wide range of topics, such as quantum algorithms, quantum data encoding, quantum feature extraction, quantum neural networks, quantum image recognition, quantum optimization, and quantum simulations. It also identifies the emerging trends, challenges, and future research directions in this rapidly evolving field. The findings of this survey report are expected to provide valuable insights for researchers, practitioners, and decision-makers interested in the field of quantum and its applications in machine learning, deep learning, image processing, and related areas. Comprehensive analysis of the research papers and the identification of the current trends and challenges in this field can serve as a valuable reference for future research and development in the quantum computing domain.
The Quantum Image Processing (QIP, based on developing along quantum computing and digital image analysis) is an emerging field. Due to the growing needs for image processing with high speed and efficiency in applications including medical imaging, satellite surveillance, autonomous vehicle, and big data analytics, there are limitations of scalability and speed with classical methods. By exploring the very principles on which the modern world operates, from superposition to entanglement, quantum computing provides an altogether transformative perspective on how to process and analyze images exponentially faster. QIP is the topic of this paper and how it might change the way that images are stored, processed, and interpreted; that is, we look at the theoretical foundations and practical considerations of QIP. Quantum representation of images, quantum Fourier transforms, and quantum edge detection are discussed. The challenges to technical and implementable aspects of QIP need not preclude the next generation of high speed computation. The objectives of this research include discovering the potential of QIP, and the weaknesses and future occurrence of the QIP technology.
Quantum Machine Learning (QML) is an interdisciplinary field combining Quantum Computing (QC) and Machine Learning (ML). It has gained increased attention due to advances in near-term hardware implementations of quantum devices. The use of QML has proven to result in a significant improvement in performance and computational speed. Consequently, QML has become an effective technique for data processing and classification. Researchers have recently proposed various QML solutions in the medical image analysis field to gain an advantage of quantum supremacy. The main objective of this work is to present a holistic review of current leading-edge published works in the quantum medical image analysis field with a focus on supervised learning using artificial neural networks. A comparative study is used to pinpoint the potential of existing techniques, the most promising techniques, and the future of research in this area.
The rapid growth of Big Data has pushed classical computing systems to their limits, necessitating innovative approaches to data processing. Quantum computing, with its inherent parallelism and entanglement capabilities, offers transformative potential for solving complex, large-scale problems that are intractable for classical systems. This review paper examines the role of quantum computing in future Big Data processing, analyzing key quantum algorithms such as Grover's search, Shor's factorization, and quantum machine learning techniques. We explore fundamental concepts including qubit operations, quantum complexity classes, and hybrid quantum-classical architectures, while critically assessing current hardware limitations like de-coherence and error rates in NISQ-era devices. The paper highlights promising applications in optimization, secure communication, and high-dimensional data analysis, alongside significant challenges in data encoding, algorithmic readiness, and practical implementation. Emerging research directions are discussed, including near-term NISQ applications, fault-tolerant quantum computing prospects, and cross-disciplinary opportunities in NLP and IoT. By synthesizing theoretical advances with practical constraints, this review provides a balanced perspective on quantum computing's evolving role in Big Data, offering insights into both its revolutionary potential and the substantial barriers that must be overcome for widespread adoption. The findings suggest that while quantum advantage remains limited to specific use cases today, continued progress in hardware and algorithms may eventually redefine the landscape of large-scale data processing. References [1] S. S. Gill et al., "Quantum computing: A taxonomy, systematic review and future directions," Softw. Pract. Exper., vol. 52, no. 1, pp. 66-114, 2022. [2] R. Ur Rasool et al., "Quantum computing for healthcare: A review," Future Internet, vol. 15, no. 3, p. 94, 2023. [3] S. Zhu et al., "Intelligent computing: The latest advances, challenges, and future," Intell. Comput., vol. 2, p. 0006, 2023. [4] M. Möller and C. Vuik, "On the impact of quantum computing technology on future developments in high-performance scientific computing," Ethics Inf. Technol., vol. 19, pp. 253-269, 2017. [5] W. Zhang et al., "Application of machine learning, deep learning and optimization algorithms in geoengineering and geoscience: Comprehensive review and future challenge," Gondwana Res., vol. 109, pp. 1-17, 2022. [6] S. D. Pasham, "Privacy-preserving data sharing in big data analytics: A distributed computing approach," Metascience, vol. 1, no. 1, pp. 149-184, 2023. [7] F. Bova, A. Goldfarb, and R. G. Melko, "Commercial applications of quantum computing," EPJ Quantum Technol., vol. 8, no. 1, p. 2, 2021. [8] U. Tariq et al., "A critical cybersecurity analysis and future research directions for the internet of things: A comprehensive review," Sensors, vol. 23, no. 8, p. 4117, 2023. [9] M. R. Haque et al., "The role of macroeconomic discourse in shaping inflation views: Measuring public trust in Federal Reserve policies," J. Bus. Insight Innov., vol. 2, no. 2, pp. 88-106, 2023. [10] M. A. Sayem et al., "AI-driven diagnostic tools: A survey of adoption and outcomes in global healthcare practices," Int. J. Recent Innov. Trends Comput. Commun., vol. 11, no. 10, pp. 1109-1122, 2023. [11] M. Z. Afshar, "Exploring factors impacting organizational adaptation capacity of Punjab Agriculture & Meat Company (PAMCO)," Int. J. Emerg. Issues Soc. Sci. Arts Humanit., vol. 2, no. 1, pp. 1-10, 2023. [12] T. A. Shaikh and R. Ali, "Quantum computing in big data analytics: A survey," in Proc. IEEE Int. Conf. Comput. Inf. Technol. (CIT), 2016, pp. 112-115. [13] A. Pandey and V. Ramesh, "Quantum computing for big data analysis," Indian J. Sci., vol. 14, no. 43, pp. 98-104, 2015. [14] J. Singh and M. Singh, "Evolution in quantum computing," in Proc. Int. Conf. Syst. Model. Adv. Res. Trends (SMART), 2016, pp. 267-270. [15] C. P. Chen and C. Y. Zhang, "Data-intensive applications, challenges, techniques and technologies: A survey on big data," Inf. Sci., vol. 275, pp. 314-347, 2014. [16] X. D. Cai et al., "Entanglement-based machine learning on a quantum computer," Phys. Rev. Lett., vol. 114, no. 11, p. 110504, 2015. [17] D. P. Acharjya and K. Ahmed, "A survey on big data analytics: Challenges, open research issues and tools," Int. J. Adv. Comput. Sci. Appl., vol. 7, no. 2, pp. 511-518, 2016. [18] S. Yu, "Big privacy: Challenges and opportunities of privacy study in the age of big data," IEEE Access, vol. 4, pp. 2751-2763, 2016. [19] O. N. Garcia and G. Bajwa, "Integration of quantum information systems in computer science and in electrical engineering," J. Integr. Des. Process Sci., vol. 19, no. 4, pp. 9-20, 2016. [20] A. JavadiAbhari et al., "ScaffCC: A framework for compilation and analysis of quantum computing programs," in Proc. 11th ACM Conf. Comput. Front., 2014, pp. 1-10. [21] S. Khanra, A. Dhir, A. N. Islam, and M. Mäntymäki, "Big data analytics in healthcare: A systematic literature review," Enterp. Inf. Syst., vol. 14, no. 7, pp. 878-912, 2020. [22] S. Ahmed, I. Ahmed, M. Kamruzzaman, and R. Saha, "Cybersecurity challenges in IT infrastructure and data management: A comprehensive review of threats, mitigation strategies, and future trend," Glob. Mainstream J. Innov. Eng. Emerg. Technol., vol. 1, no. 1, pp. 36-61, 2022. [23] O. M. Araz, T. M. Choi, D. L. Olson, and F. S. Salman, "Role of analytics for operational risk management in the era of big data," Decis. Sci., vol. 51, no. 6, pp. 1320-1346, 2020. [24] R. Orús, S. Mugel, and E. Lizaso, "Quantum computing for finance: Overview and prospects," Rev. Phys., vol. 4, p. 100028, 2019. [25] D. Herman et al., "A survey of quantum computing for finance," arXiv:2201.02773, 2022. [26] S. S. Gill et al., "AI for next generation computing: Emerging trends and future directions," Internet Things, vol. 19, p. 100514, 2022. [27] A. Deshmukh, D. S. Patil, P. D. Pawar, and S. Kumari, "Recent trends for smart environments with AI and IoT-based technologies: A comprehensive review," in Handbook of Research on Quantum Computing for Smart Environments, 2023, pp. 435-452. [28] S. J. Nawaz et al., "Quantum machine learning for 6G communication networks: State-of-the-art and vision for the future," IEEE Access, vol. 7, pp. 46317-46350, 2019. [29] M. Asif, "Integration of Information Technology in Financial Services and its Adoption by the Financial Sector in Pakistan," Inverge J. Soc. Sci., vol. 1, no. 2, pp. 23-35, 2022.