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Computer Science & IT Research Journal

QUANTUM COMPUTING IN BIG DATA ANALYTICS: A COMPREHENSIVE REVIEW: ASSESSING THE ADVANCEMENTS, CHALLENGES, AND POTENTIAL IMPLICATIONS OF QUANTUM APPROACHES IN HANDLING MASSIVE DATA SETS

Akoh Atadoga, Ogugua Chimezie Obi, Femi Osasona, Shedrack Onwusinkwue, Shedrack Onwusinkwue, Andrew Ifesinachi Daraojimba, Samuel Onimisi Dawodu

Published Feb 18, 2024• 9 citations
Publisher

Abstract

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.

DOI
10.51594/csitrj.v5i2.794

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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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This article explores the transformative potential of quantum computing in the field of business analytics. It begins with an introduction to quantum computing, explaining its fundamental principles and recent advancements. The study highlights the limitations of current business analytics methods and demonstrates how quantum computing could address these limitations by offering enhanced data processing capabilities, advanced algorithms, and solutions to complex optimization problems. A comprehensive literature review is conducted to provide context and identify gaps in the existing research. The article then outlines a research design that incorporates both real-world and simulated data, using online datasets and quantum computing frameworks for analysis. The findings reveal significant opportunities for quantum computing to revolutionize business analytics, including improved efficiency, accuracy, and the ability tosolve previously intractable problems. However, the article also addresses key challenges such as technical limitations, cost, accessibility, and integration issues. The discussion highlights emerging trends and provides strategic recommendations for businesses considering the adoption of quantum computing. The article concludes with a summary of the implications of integrating quantum computing into business analytics and reflects onfuture potential and challenges.
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Quantum computing represents a revolutionary paradigm shift in data processing, promising to revolutionize industries, solve complex computational problems, and propel scientific discovery to unprecedented heights. This research paper provides a comprehensive overview of the current state of quantum computing technology, its basic principles, and applications. The article begins by explaining the basic principles of quantum mechanics, introducing key concepts such as superposition and entrainment that underlie the enormous computing power of quantum computers. The discussion of quantum bits, or qubits, highlights the unique properties of quantum computers that allow them to perform tasks much faster than classical computers. Research papers cover various quantum computer architectures, gate-based quantum computers, adiabatic quantum computers, and quantum annealing devices. Explores challenges and advances in hardware with attention to quantum error correction and quantum breakthroughs. In addition, this paper explores various quantum algorithms and their applications in domains such as cryptography, optimization, material science, and drug discovery. It reviews progress in algorithm development and emphasizes the practical implications of quantum computing for solving real-world problems. Additionally, the research document examines the current state of the Quantum Computing industry and highlights the key players and their contributions to the field. It also touches on the economic and ethical implications of quantum computers, considering security issues and implications for existing computing paradigms. In summary, this comprehensive review reveals the transformative potential of quantum computers and offers valuable insights into current opportunities, challenges, and future prospects. As quantum computing advances, this paper serves as a valuable resource for researchers, policymakers, and industry leaders who want to understand and harness the power of quantum computing.
Quantum computing represents a paradigm shift in computational power, leveraging quantum mechanics principles to solve complex problems beyond the reach of classical computers. As quantum technologies evolve, they hold the promise of revolutionizing industries ranging from cryptography and artificial intelligence to material science and drug discovery. This article explores the current developments in quantum computing, highlighting the potential future implications for various sectors. The article discusses the state-of-the-art quantum computing techniques, key challenges in scalability and error correction, and the potential for real-world applications. The implications of quantum computing on cryptography and data security, coupled with advancements in quantum algorithms, are also examined. The future of quantum computing is bright, but significant challenges remain that will require collaboration across interdisciplinary fields.