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Contemporary Physics

The development of quantum theory

Ronald F. Peierls

Published Jan 1, 2009• 25 citations
Publisher

Abstract

(1965). The development of quantum theory. Contemporary Physics: Vol. 6, No. 3, pp. 192-205.
DOI
10.1080/00107510902734763

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The rapid expansion of Big Data has strained classical computing systems, necessitating innovative solutions for efficient data processing. Quantum computing, leveraging principles like superposition and entanglement, presents transformative potential for addressing complex, large-scale problems that are intractable for classical methods. This review explores the role of quantum computing in Big Data processing, examining key algorithms such as Grover’s search, Shor’s factorization, and quantum machine learning techniques. It discusses foundational concepts like qubit operations, quantum complexity classes, and hybrid quantum-classical architectures, while critically assessing hardware limitations such as de-coherence and error rates in NISQ-era devices. Promising applications in optimization, secure communication, and high-dimensional data analysis are highlighted, alongside challenges in data encoding, algorithmic readiness, and practical implementation. 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Shanmugasundaram, V. Aswini, and G. Suganya, "A comprehensive review on cloud computing security," in 2017 Int. Conf. Innov. Inf., Embedded Commun. Syst. (ICIIECS), 2017, pp. 1–5. [8]. N. Subramanian and A. Jeyaraj, "Recent security challenges in cloud computing," Comput. Electr. Eng., vol. 71, pp. 28–42, 2018. [9]. G. Ramachandra, M. Iftikhar, and F. A. Khan, "A comprehensive survey on security in cloud computing," Procedia Comput. Sci., vol. 110, pp. 465–472, 2017. [10]. W. Ahmad et al., "Cyber security in IoT-based cloud computing: A comprehensive survey," Electronics, vol. 11, no. 1, p. 16, 2021. [11]. H. Tabrizchi and M. Kuchaki Rafsanjani, "A survey on security challenges in cloud computing: Issues, threats, and solutions," J. Supercomput., vol. 76, no. 12, pp. 9493–9532, 2020. [12]. L. Coppolino et al., "Cloud security: Emerging threats and current solutions," Comput. Electr. Eng., vol. 59, pp. 126–140, 2017. [13]. M. F. 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•Aug 10, 2020•6 citations
The rapid expansion of Big Data has strained classical computing systems, necessitating innovative solutions for efficient data processing. Quantum computing, leveraging principles like superposition and entanglement, presents transformative potential for addressing complex, large-scale problems that are intractable for classical methods. This review explores the role of quantum computing in Big Data processing, examining key algorithms such as Grover’s search, Shor’s factorization, and quantum machine learning techniques. It discusses foundational concepts like qubit operations, quantum complexity classes, and hybrid quantum-classical architectures, while critically assessing hardware limitations such as de-coherence and error rates in NISQ-era devices. Promising applications in optimization, secure communication, and high-dimensional data analysis are highlighted, alongside challenges in data encoding, algorithmic readiness, and practical implementation. Emerging research directions, including near-term NISQ applications, fault-tolerant quantum computing, and cross-disciplinary opportunities in NLP and IoT, are also explored. By synthesizing theoretical advances with practical constraints, this review provides a balanced perspective on quantum computing is potential to reshape Big Data analytics, emphasizing both its revolutionary capabilities and the significant barriers to widespread adoption. While quantum advantage remains limited to specific use cases today, continued progress in hardware and algorithms may redefine large-scale data processing in the future. References [1]. C. Stouffer, "23 cloud security risks, threats, and best practices," Norton, 2023. [Online]. Available: https://us.norton.com/blog/privacy/cloud-security-risks [2]. A. I. Tahirkheli et al., "A survey on modern cloud computing security over smart city networks: Threats, vulnerabilities, consequences, countermeasures, and challenges," Electronics, vol. 10, no. 15, p. 1811, 2021. [3]. Biz Technology Solutions, "8 cloud computing security challenges," Biz Technology Solutions, Jan. 27, 2023. [Online]. Available: https://biztechnologysolutions.com/cloud-computing-security-challenges/ [4]. S. P, "New cloud security threats in 2023: How to stay protected," Cymune-Blogs, 2023. [Online]. Available: https://www.cymune.com/blog-details/cloud-security-threats-in-2023-and-how-to-stay-protected [5]. S. El Kafhali, I. El Mir, and M. Hanini, "Security threats, defense mechanisms, challenges, and future directions in cloud computing," Arch. Comput. Methods Eng., vol. 29, no. 1, pp. 223–246, 2022. [6]. J. J. Ang'udi, "Security challenges in cloud computing: A comprehensive analysis," World J. Adv. Eng. Technol. Sci., vol. 10, no. 2, pp. 155–181, 2023. [7]. G. Shanmugasundaram, V. Aswini, and G. Suganya, "A comprehensive review on cloud computing security," in 2017 Int. Conf. Innov. Inf., Embedded Commun. Syst. (ICIIECS), 2017, pp. 1–5. [8]. N. Subramanian and A. Jeyaraj, "Recent security challenges in cloud computing," Comput. Electr. Eng., vol. 71, pp. 28–42, 2018. [9]. G. Ramachandra, M. Iftikhar, and F. A. Khan, "A comprehensive survey on security in cloud computing," Procedia Comput. Sci., vol. 110, pp. 465–472, 2017. [10]. W. Ahmad et al., "Cyber security in IoT-based cloud computing: A comprehensive survey," Electronics, vol. 11, no. 1, p. 16, 2021. [11]. H. Tabrizchi and M. Kuchaki Rafsanjani, "A survey on security challenges in cloud computing: Issues, threats, and solutions," J. Supercomput., vol. 76, no. 12, pp. 9493–9532, 2020. [12]. L. Coppolino et al., "Cloud security: Emerging threats and current solutions," Comput. Electr. Eng., vol. 59, pp. 126–140, 2017. [13]. M. F. Mushtaq et al., "Cloud computing environment and security challenges: A review," Int. J. Adv. Comput. Sci. Appl., vol. 8, no. 10, 2017. [14]. Y. Li and Q. Liu, "A comprehensive review study of cyber-attacks and cyber security; Emerging trends and recent developments," Energy Rep., vol. 7, pp. 8176–8186, 2021. [15]. Ö. Aslan et al., "A comprehensive review of cyber security vulnerabilities, threats, attacks, and solutions," Electronics, vol. 12, no. 6, p. 1333, 2023. [16]. S. Basu et al., "Cloud computing security challenges & solutions—A survey," in 2018 IEEE 8th Annu. Comput. Commun. Workshop Conf. (CCWC), 2018, pp. 347–356. [17]. C. Butpheng, K. H. Yeh, and H. Xiong, "Security and privacy in IoT-cloud-based e-health systems—A comprehensive review," Symmetry, vol. 12, no. 7, p. 1191, 2020. [18]. 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. [19]. 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. [20]. 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. [21]. B. Alouffi et al., "A systematic literature review on cloud computing security: Threats and mitigation strategies," IEEE Access, vol. 9, pp. 57792–57807, 2021. [22]. U. A. Butt et al., "A review of machine learning algorithms for cloud computing security," Electronics, vol. 9, no. 9, p. 1379, 2020. [23]. O. Can et al., "A comprehensive literature of genetics cryptographic algorithms for data security in cloud computing," Cybern. Syst., pp. 1–35, 2023. [24]. M. Javaid, A. Haleem, R. P. Singh, and R. Suman, "Towards insighting cybersecurity for healthcare domains: A comprehensive review of recent practices and trends," Cybersecur. Appl., vol. 1, p. 100016, 2023. [25]. M. M. Sadeeq et al., "IoT and Cloud computing issues, challenges and opportunities: A review," Qubahan Acad. J., vol. 1, no. 2, pp. 1–7, 2021. [26]. A. A. Abbasi et al., "Software-defined cloud computing: A systematic review on latest trends and developments," IEEE Access, vol. 7, pp. 93294–93314, 2019. [27]. A. Heidari and M. A. Jabraeil Jamali, "Internet of Things intrusion detection systems: A comprehensive review and future directions," Cluster Comput., vol. 26, no. 6, pp. 3753–3780, 2023. [28]. 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. 01, pp. 36–61, 2022. [29]. O. I. Abiodun et al., "Comprehensive review of artificial neural network applications to pattern recognition," IEEE Access, vol. 7, pp. 158820–158846, 2019. [30]. A. Fahim, M. Hasan, and M. A. Chowdhury, "Smart parking systems: Comprehensive review based on various aspects," Heliyon, vol. 7, no. 5, 2021. [31]. A. Khanna and S. Kaur, "Internet of things (IoT), applications and challenges: A comprehensive review," Wirel. Pers. Commun., vol. 114, pp. 1687–1762, 2020. [32]. A. K. Jain, S. R. Sahoo, and J. Kaubiyal, "Online social networks security and privacy: Comprehensive review and analysis," Complex Intell. Syst., vol. 7, no. 5, pp. 2157–2177, 2021. [33]. L. Tightiz and H. Yang, "A comprehensive review on IoT protocols’ features in smart grid communication," Energies, vol. 13, no. 11, p. 2762, 2020. [34]. P. J. Sun, "Privacy protection and data security in cloud computing: A survey, challenges, and solutions," IEEE Access, vol. 7, pp. 147420–147452, 2019. [35]. U. Agarwal et al., "Blockchain technology for secure supply chain management: A comprehensive review," IEEE Access, vol. 10, pp. 85493–85517, 2022. [36]. M. Amani et al., "Google earth engine cloud computing platform for remote sensing big data applications: A comprehensive review," IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., vol. 13, pp. 5326–5350, 2020.
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. 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