A Comprehensive Review on Quantum Intelligence: Synergizing AI and Quantum Computing

  • Unique Paper ID: 186500
  • PageNo: 1895-1899
  • Abstract:
  • Quantum computing and artificial intelligence (AI) are two rapidly evolving technologies poised to redefine the future of computation and decision-making. The two fields unite under the name Quantum Artificial Intelligence (QAI) or Quantum Intelligence, which enables a transformative approach to problem-solving through the fusion of AI mental functions with quantum computing capabilities. This paper provides a comprehensive review of the current landscape, highlighting the opportunities and challenges in integrating quantum computing with AI systems. The research investigates current developments in quantum machine learning, quantum neural networks, and hybrid quantum-classical systems, which show promise for achieving rapid performance improvements and enhanced scalability and data processing capabilities. At the same time, it identifies critical barriers, including hardware limitations, algorithmic complexity, data encoding issues, and the lack of standardization in frameworks. The review further explores future directions, including the role of explainable AI, error correction, and interdisciplinary research in achieving practical quantum–AI synergy. By analyzing both the technological potential and the practical constraints, this paper aims to outline a roadmap for advancing quantum intelligence as a transformative force in next-generation computational paradigms.

Copyright & License

Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

BibTeX

@article{186500,
        author = {Mrs. K. Krishna Veni and G. Rajesh Pradeep},
        title = {A Comprehensive Review on Quantum Intelligence: Synergizing AI and Quantum Computing},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {6},
        pages = {1895-1899},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=186500},
        abstract = {Quantum computing and artificial intelligence (AI) are two rapidly evolving technologies poised to redefine the future of computation and decision-making. The two fields unite under the name Quantum Artificial Intelligence (QAI) or Quantum Intelligence, which enables a transformative approach to problem-solving through the fusion of AI mental functions with quantum computing capabilities. This paper provides a comprehensive review of the current landscape, highlighting the opportunities and challenges in integrating quantum computing with AI systems. The research investigates current developments in quantum machine learning, quantum neural networks, and hybrid quantum-classical systems, which show promise for achieving rapid performance improvements and enhanced scalability and data processing capabilities. At the same time, it identifies critical barriers, including hardware limitations, algorithmic complexity, data encoding issues, and the lack of standardization in frameworks. The review further explores future directions, including the role of explainable AI, error correction, and interdisciplinary research in achieving practical quantum–AI synergy. By analyzing both the technological potential and the practical constraints, this paper aims to outline a roadmap for advancing quantum intelligence as a transformative force in next-generation computational paradigms.},
        keywords = {Quantum Computing, Artificial Intelligence, Quantum Intelligence, Quantum Machine Learning, Hybrid Quantum-Classical Systems, Quantum Algorithms, Technological Integration.},
        month = {November},
        }

Cite This Article

Veni, M. K. K., & Pradeep, G. R. (2025). A Comprehensive Review on Quantum Intelligence: Synergizing AI and Quantum Computing. International Journal of Innovative Research in Technology (IJIRT), 12(6), 1895–1899.

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