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Quantum Computing in Artificial Intelligence: Convergence, Frameworks, and Emerging Application
Subject area: Science,Engineering and Technology · Area of research: Quantum Artificial Intelligence
DOI: 10.64388/IREV10I3-1723025
Abstract
The rapid convergence of quantum computing and artificial intelligence is reshaping computational paradigms across cryptography, optimization, sensing, and intelligent systems. This paper surveys and synthesizes recent developments at the intersection of quantum computing and AI, with particular attention to superconducting quantum hardware, quantum reinforcement learning, quantum key distribution optimization, and hybrid AI-driven post-quantum security frameworks. Drawing upon 2026 literature — including advances in high-speed predistortion architectures for flux-bias lines [1], enhanced quantum key distribution parameter optimization [2], quantum reinforcement learning for spectrum allocation in 6G networks [3], hybrid AI–post-quantum encryption for Internet of Medical Things (IoMT) systems [4], and measurement-consistent low-field MRI enhancement via k-space noise modeling [5] — we present a unified perspective on how quantum resources augment classical AI and how AI, in turn, accelerates the practical deployment of quantum technologies. We introduce a conceptual Quantum–AI Convergence Framework that maps hardware constraints, algorithmic opportunities, and security requirements. The analysis highlights persistent challenges in noise resilience, scalability, hybrid orchestration, and regulatory readiness, while outlining promising research directions for trustworthy quantum-enhanced intelligence.
Keywords
Quantum Computing, Artificial Intelligence, Quantum Machine Learning, Quantum Reinforcement Learning, Post-Quantum Cryptography, Quantum Key Distribution, Superconducting Qubits, 6G Networks, Internet of Medical Things, Hybrid Quantum–Classical Systems
References
[1] Ma, M., Chen, Y., Tian, L., & Wu, Q. (2026). High-Speed Parallel Predistortion Architecture for Z-Flux Bias Lines in Superconducting Quantum Computing. In Proc. 2026 IEEE 5th International Conference on Information Technology, Big Data and Artificial Intelligence (ICIBA).
[2] Ying, H., & Fan, J. (2026). An Enhanced Algorithm Framework for Optimizing Quantum Key Distribution Parameters. In Proc. 2026 7th Information Communication Technologies Conference (ICTC).
[3] Kannan, K., Al-Shalout, M. I., Valarmathi, T. S., Aluri, S., Verma, V., & Shymalabharathi, P. (2026). Quantum Reinforcement Learning Assisted Spectrum Allocation Framework for Sixth Generation Networks. In Proc. 2026 9th International Conference on Circuit, Power & Computing Technologies (ICCPCT). Crossref
[4] Prabhu, T., Kumar, J. A., Anushia, R. M., & Shahila, D. F. D. (2026). A Hybrid AI-Driven Post-Quantum Encryption Framework for Secure Medical Data Transmission in IoMT Systems. In Proc. 2026 9th International Conference on Circuit, Power & Computing Technologies (ICCPCT). Crossref
[5] Hu, Y., Liu, Q., Zhou, T., Li, Z., Liang, P., Zhang, J., Lu, R., Lou, X., Zheng, J., Yang, J., & Chen, Y. (2026). Measurement-Consistent Low-Field MRI Enhancement via k-Space Noise Modeling. IEEE Transactions on Instrumentation and Measurement, early access. Crossref
How to cite this paper
@article{1723025,
author = {Albert James, Dr. Densy John Vadakkan},
title = {Quantum Computing in Artificial Intelligence: Convergence, Frameworks, and Emerging Application},
journal = {Iconic Research And Engineering Journals},
year = {2026},
volume = {10},
number = {3},
pages = {1258-1262},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1723025.pdf},
abstract = {The rapid convergence of quantum computing and artificial intelligence is reshaping computational paradigms across cryptography, optimization, sensing, and intelligent systems. This paper surveys and synthesizes recent developments at the intersection of quantum computing and AI, with particular attention to superconducting quantum hardware, quantum reinforcement learning, quantum key distribution optimization, and hybrid AI-driven post-quantum security frameworks. Drawing upon 2026 literature — including advances in high-speed predistortion architectures for flux-bias lines [1], enhanced quantum key distribution parameter optimization [2], quantum reinforcement learning for spectrum allocation in 6G networks [3], hybrid AI–post-quantum encryption for Internet of Medical Things (IoMT) systems [4], and measurement-consistent low-field MRI enhancement via k-space noise modeling [5] — we present a unified perspective on how quantum resources augment classical AI and how AI, in turn, accelerates the practical deployment of quantum technologies. We introduce a conceptual Quantum–AI Convergence Framework that maps hardware constraints, algorithmic opportunities, and security requirements. The analysis highlights persistent challenges in noise resilience, scalability, hybrid orchestration, and regulatory readiness, while outlining promising research directions for trustworthy quantum-enhanced intelligence.},
keywords = {Quantum Computing, Artificial Intelligence, Quantum Machine Learning, Quantum Reinforcement Learning, Post-Quantum Cryptography, Quantum Key Distribution, Superconducting Qubits, 6G Networks, Internet of Medical Things, Hybrid Quantum–Classical Systems},
month = {September},
doi = {https://doi.org/10.64388/IREV10I3-1723025}
}