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IEEE International Conference on Quantum Photonics, Artificial Intelligence & Networking (QPAIN)

Quantum Computing for Big Data and AI: Emerging Applications, Technical Challenges, and Future Directions

Abdus Sobhan, Md Tanvir Islam Sourav, Nasrullah Masud, Abdullah Al Bassam

Published Jul 31, 2025• 3 citations
Publisher

Abstract

Fast data and growing artificial intelligence have brought traditional computer infrastructures to their limits. They expose inherent limitations in processing speed, energy efficiency, and scalability. In the face of nearing physical barriers with Moore's Law and memory bottlenecks plaguing traditional von Neumann architectures, quantum computing offers a completely new way of leveraging principles such as superposition and entanglement to redefine computation possibilities. This review is concerned with addressing how quantum computing stands to do that foundational AI and big data challenges, from accelerating extremely complex optimization tasks to enabling very secure, real-time analytics. These topics include basic principles of quantum mechanics, the basis of qubits and quantum circuits; updates in quantum algorithms (such as Grover's search, quantum annealing); and hybrid classical-quantum systems that serve as pathways through technological gaps. The paper suggests major applications of transformative nature in some of the higher-impact domains: quantum-accelerated drug discovery for health, portfolio optimization in finance, and quantum-secured communication infrastructures to guard the IoT. Case studies present early successes on quantum machine learning models improving diagnostic accuracy and annealing-based traffic optimization for urban congestion. Decoherence, error-prone devices, as well as low software ecosystems are conspiring against the actual roadways to practical quantum advantages. Future progress is dependent on the innovative development of error-corrected qubits, scalable algorithms, as well as interdisciplinary collaboration. While universal quantum supremacy remains while unreached, this coming decade promises a series of developments toward applied, specific applications that will place quantum computing as an added force among the classical systems. This review tries to project both sides of the likely potential of the space, obstacles, and path, paving the way for the researcher to find their foot in the continually progressing quantum-AI terrain.

DOI
10.1109/qpain66474.2025.11171694

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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
Fast data and growing artificial intelligence have brought traditional computer infrastructures to their limits. They expose inherent limitations in processing speed, energy efficiency, and scalability. In the face of nearing physical barriers with Moore's Law and memory bottlenecks plaguing traditional von Neumann architectures, quantum computing offers a completely new way of leveraging principles such as superposition and entanglement to redefine computation possibilities. This review is concerned with addressing how quantum computing stands to do that foundational AI and big data challenges, from accelerating extremely complex optimization tasks to enabling very secure, real-time analytics. These topics include basic principles of quantum mechanics, the basis of qubits and quantum circuits; updates in quantum algorithms (such as Grover's search, quantum annealing); and hybrid classical-quantum systems that serve as pathways through technological gaps. The paper suggests major applications of transformative nature in some of the higher-impact domains: quantum-accelerated drug discovery for health, portfolio optimization in finance, and quantum-secured communication infrastructures to guard the IoT. Case studies present early successes on quantum machine learning models improving diagnostic accuracy and annealing-based traffic optimization for urban congestion. Decoherence, error-prone devices, as well as low software ecosystems are conspiring against the actual roadways to practical quantum advantages. Future progress is dependent on the innovative development of error-corrected qubits, scalable algorithms, as well as interdisciplinary collaboration. While universal quantum supremacy remains while unreached, this coming decade promises a series of developments toward applied, specific applications that will place quantum computing as an added force among the classical systems. This review tries to project both sides of the likely potential of the space, obstacles, and path, paving the way for the researcher to find their foot in the continually progressing quantum-AI terrain.
Quantum computing has emerged as a transformative computational paradigm capable of addressing limitations increasingly encountered by classical computing systems as Moore’s law approaches physical and economic constraints. By leveraging quantum mechanical principles such as superposition, entanglement, and interference, quantum systems offer the potential for significant computational advantages in solving complex problems that are currently infeasible for conventional architectures. This review critically synthesizes recent advances in quantum computing by examining foundational principles, major algorithmic breakthroughs, evolving hardware platforms, and interdisciplinary real-world applications. A structured narrative literature review was conducted using major scientific databases, including PubMed, IEEE Xplore, Nature, ScienceDirect, and arXiv, with emphasis on peer-reviewed studies, landmark foundational research, and major industry developments published between 2018 and 2026. Key findings indicate substantial progress in superconducting, trapped ion, photonic, and topological quantum systems, alongside innovations in algorithms such as Shor’s, Grover’s, variational quantum methods, and quantum machine learning. Despite these advancements, major challenges including decoherence, noise, scalability limitations, and fault-tolerant error correction remain substantial barriers to commercial maturity. Future implications suggest quantum computing may profoundly influence artificial intelligence, cybersecurity, optimization, molecular simulation, and scientific discovery, particularly through hybrid classical-quantum systems and cloud-based democratization. Although practical deployment remains transitional, quantum computing is positioned to become a foundational technology with broad scientific, industrial, and societal implications.
Quantum computing is poised to revolutionize the future of advanced computational systems, offering unprecedented speed and efficiency for solving complex problems that are beyond the capabilities of classical computers. Traditional computational models, based on bits representing either a 0 or 1, are approaching their physical and performance limits. Quantum computers, however, leverage the principles of quantum mechanics—specifically, superposition and entanglement—to operate on quantum bits, or qubits, which can exist in multiple states simultaneously. This capability allows quantum systems to process vast amounts of data in parallel, potentially solving problems in seconds that would take classical systems millions of years. As the demands on computational systems grow in fields like cryptography, artificial intelligence (AI), drug discovery, and materials science, quantum computing offers solutions that can overcome the bottlenecks faced by classical computing architectures. This paper explores the foundational principles of quantum computing, its advantages over classical systems, and its potential applications in various industries. Additionally, we analyze the current challenges that quantum computing faces, such as error rates, quantum decoherence, and hardware scalability. We also consider the future trajectory of quantum computing, including the potential for quantum supremacy and the development of hybrid quantum-classical systems. Although quantum computing is still in its nascent stages, with practical quantum computers requiring further advancements in qubit coherence and error correction, its future impact on computational systems is undeniable. This paper aims to provide a comprehensive review of how quantum computing is shaping the future of computational systems, detailing the technologies, applications, and challenges ahead. With continuous technological advancements, quantum computing has the potential to redefine global industries, solve previously unsolvable problems, and fundamentally alter our understanding of computation in the coming decades.
This paper explores the convergence of quantum computing and artificial intelligence (AI), examining how their integration may redefine computational paradigms. Quantum computing, with its unique properties of superposition and entanglement, has the potential to exponentially accelerate AI processes, particularly in optimization, machine learning, and data analysis. We investigate quantum algorithms, such as the quantum Fourier transform and Grover’s algorithm, highlighting their application to AI models and machine learning tasks that require vast computational resources. The paper further delves into hybrid quantum-classical approaches, which leverage the strengths of both domains to address real-world problems. Challenges, such as quantum error correction, scalability, and the need for specialized hardware, are also discussed. We provide an analysis of ongoing advancements in quantum AI, including quantum-enhanced neural networks and reinforcement learning, and their implications for fields like natural language processing and predictive analytics. This research emphasizes the transformative potential of quantum AI while acknowledging the significant technical hurdles that remain. The integration of quantum computing and AI promises to unlock unprecedented computational capabilities, paving the way for breakthroughs in scientific research, industry applications, and complex problem-solving.
The exponential growth of big data in artificial intelligence (AI) has posed significant challenges for scaling machine learning (ML) models efficiently. Classical computing methods struggle to process vast datasets and optimize complex ML models in a feasible timeframe. Quantum computing offers a promising paradigm shift by leveraging quantum mechanics to perform computations at unprecedented scales and speeds. This study explores the role of quantum computing in addressing the big data problem in AI, with a focus on its potential to enhance the scalability of ML models. Key topics include quantum-enhanced data processing, optimization of large-scale algorithms, and advancements in quantum machine learning (QML). We analyze the theoretical benefits of quantum approaches, such as faster matrix inversion and efficient kernel methods, alongside practical developments in quantum hardware and hybrid classical-quantum frameworks. The study further highlights the challenges, such as noise, hardware limitations, and the gap between theoretical and practical implementations. By bridging quantum computing with AI, this research identifies avenues for accelerating innovation in scalable ML, paving the way for breakthroughs in data-intensive applications. KEYWORDS: Quantum computing, artificial intelligence, big data, machine learning, scalability, quantum machine learning (QML), data processing, optimization, hybrid quantum-classical frameworks
Quantum computing represents a transformative shift from classical computational paradigms by utilizing quantum mechanical principles such as superposition, entanglement, and quantum interference.In the era of Artificial Intelligence (AI), the integration of quantum computing with information technology opens new possibilities for solving complex computational problems beyond the capabilities of traditional systems.This paper explores the conceptual foundations of quantum computing and its convergence with AI-driven information technologies.It examines how quantum algorithms enhance machine learning efficiency, optimize large-scale data processing, and strengthen cybersecurity through quantum cryptography.Furthermore, the study discusses current technological challenges including hardware instability, error correction, scalability issues, and the need for interdisciplinary expertise.Using analytical and secondary data sources, the paper evaluates emerging applications in healthcare, finance, communication networks, and scientific simulations.The research concludes that quantum-AI integration will redefine computational intelligence, enabling faster decision-making and innovative technological solutions.The study highlights future research directions and emphasizes the importance of national-level academic collaboration to accelerate quantum technology adoption.
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. 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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.