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CRC Press eBooks

Quantum Computation for Big Information Processing

Tawseef Ayoub Shaikh, Rashid Ali

Published Sep 4, 2020• 2 citations
Publisher

Abstract

Big data is a hot buzzword that has impacted every field nowadays. Big data applies to applications that are beyond storage, processing and analyzing capabilities of traditional processes and tools. Big data is the gold mine of data piled up and to mine it for useful insights, brands it both opportunistic and challenging at one go. The healthcare sector is the best example of big data, where data is not only produced in huge quantities but is very complex and diverse because of the varied formats of the data. In such a scenario, quantum computing offers promises of rescue by providing new features in information processing systems, particularly in big data analytics. In this study, we have proposed a framework of how the power of quantum computing can be used for solving the big data problem particularly focusing it on healthcare data sector. A brief review of quantum computing techniques is also carried in the form of literature review. The challenges, promises, future directions, and techniques of quantum computing in big data analytics are also highlighted.

DOI
10.1201/9780429352546-13

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Big data is a hot buzzword that has impacted every field nowadays. Big data applies to applications that are beyond storage, processing and analyzing capabilities of traditional processes and tools. Big data is the gold mine of data piled up and to mine it for useful insights, brands it both opportunistic and challenging at one go. The healthcare sector is the best example of big data, where data is not only produced in huge quantities but is very complex and diverse because of the varied formats of the data. In such a scenario, quantum computing offers promises of rescue by providing new features in information processing systems, particularly in big data analytics. In this study, we have proposed a framework of how the power of quantum computing can be used for solving the big data problem particularly focusing it on healthcare data sector. A brief review of quantum computing techniques is also carried in the form of literature review. The challenges, promises, future directions, and techniques of quantum computing in big data analytics are also highlighted.
Big Data is a term which denotes data that is beyond storage capacity and processing capabilities of classical computer and getting some insight from large amount of data is a very big challenge at hand. Quantum Computing comes to rescue by offering a lot of promises in information processing systems, particularly in Big Data Analytics. In this paper, we have reviewed the available literature on Big Data Analytics using Quantum Computing for Machine Learning and its current state of the art. We categorized the Quantum Machine learning in different subfields depending upon the logic of their learning followed by a review in each technique. Quantum Walks used to construct Quantum Artificial Neural Networks, which exponentially speed-up the quantum machine learning algorithm is discussed. Quantum Supervised and Unsupervised machine learning and its benefits are compared with that of Classical counterpart. The limitations of some of the existing Machine learning techniques and tools are enunciated, and the significance of Quantum computing in Big Data Analytics is incorporated. Being in its infancy as a totally new field, Quantum computing comes up with a lot of open challenges as well. The challenges, promises, future directions and techniques of the Quantum Computing in Machine Learning are also highlighted.
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.
Big data is a term that refers to data that exceeds the storage capacity and processing capability of traditional computers, and gaining insight from vast amounts of data is a significant task. Quantum computing saves the day by making numerous promises in information processing systems, including big data insights. Quantum computers are machines that employ quantum physics to store and process data. This could be quite useful for particular applications, where they can significantly outperform even our most powerful supercomputers. In typical computers such as laptops and smartphones, data is stored in binary “bits,” which can be either 0s or 1s. A quantum bit, or qubit, is the basic memory unit of a quantum computer. Regardless of the fact that quantum computing is a relatively new technology, it is already being employed in data analytics. This chapter explains the review of quantum computing and big data, with challenges, analytics, and future.
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. 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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. 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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.
Quantum computing is an emerging paradigm that will soon transform the face of several industries, such as health care. This paper focuses on applying quantum computing in optimizing analytics on healthcare data, especially amidst big data challenges in the digital age. A health system generates tremendous amounts of complex datasets requiring advanced tools to draw meaningful insights from such data. Traditionally, most of these methods fail to meet the demands for real-time processing, predictiveness, and data security. Taking leaps with extraordinary computing power from quantum computing, health analytics can be taken towards leaps of revolutionary precision in medicine, resource allocation, and early detection of diseases. This paper explains how quantum algorithms synergistically find applications in health data. Areas of applicability, technological constraints, and issues related to ethics form other emphases. This, in turn, provides a comprehensive literature review, a method for quantum-enhanced analytics, and an analysis of the results of simulated quantum experiments, which points toward this integration as transformative. Furthermore, an architectural framework for quantum-driven healthcare systems, as depicted in the study, is postulated. The results are illustrated, including figures and corresponding tables for descriptive performance benefits and challenges. Implications to clinical workflows, patient outcomes, and the larger health ecosystem are demonstrated in the discussion. The conclusion presents the limitations and avenues for further research focusing on interdisciplinarity with quantum computing for the best healthcare.
We live in the data age. By big data, we mean gigantic and sophisticated data that are difficult to store and analyze using conventional computers. In this modern world, everything boils down to playing around with data. Data is produced from everything we know and from every activity we perform. It is generated from every gadget we use. Big data is vital as it makes it possible for organizations to accumulate, save, and handle colossal quantities of data at the appropriate rate and pulse in order to extract insights. Cloud platforms play a crucial role in big data analysis by providing scalable repositories, powerful computing resources, and affordable solutions for handling this enormous data. Business Intelligence, identifying trends, inferring, and predicting heavily rely on big data analytics. Nowadays, many big data analytic tools are available. However, there are still issues that cannot be solved by even the most refined conventional methods in a stipulated time. The idea of quantum computing was created to offer contemporary ordinary computers access to huge processing power. As quantum computing technology progresses rapidly, it provides a groundbreaking opportunity to enhance cloud computing platforms by carrying out these enormous workloads that classical systems cannot handle. Quantum Cloud Computing is a developing model of computing that enables quantum applications and helps users to operate on quantum computers without being restricted to a physical environment. Some findings related to the concepts of cloud computing, quantum computing, etc., are presented.