Quantum Machine Learning Models: Principles, Frameworks, and Computational Challenges

Authors

• K. A. Jayabalaji, Sri Krishna College of Engineering and Technology
• S. Venkata Anand, Vignan Institute of Information and Technology
• Dineshkumar Rajendran, Old Dominion UniversityFollow
• Prasanta Chatterjee Biswas, Parul University
• Sardor Omonov, Tashkent State University of Economics
• Rubaid Ashfaq, Amity School of Communication

Document Type

Book Chapter

Publication Date

2026

DOI

10.4018/979-8-3373-7779-7.ch005

Publication Title

Emerging Hybrid Models for Neuromorphic AI and Quantum Computing

Pages

137-168

Abstract

Quantum machine learning (QML) has become an optimistic avenue of harnessing quantum computation in data-driven modeling, especially of issues with high dimensionality and complicated correlations. Current methods are generally based on fixed or over-parameterized quantum circuits, and hence restricted to scalability as well as unproductive optimization in real-world hardware. This chapter introduces a hybrid quantum-classical learning system that is adaptive and provides principled quantum data encoding, architecture-conscious variational circuit design and resource-optimal optimization. The technique is based on the concepts of quantum architecture search and subspace-preserving transformations to trade expressiveness with trainability, and discretize the quantum model into a classical pipeline processing system to make it robust and flexible. The experimental analysis proves that the suggested framework is more accurate in its classification and converges more quickly than the representative variational and convolutional quantum models.

Rights

© 2026 by IGI Global Scientific Publishing

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Original Publication Citation

Jayabalaji, K. A., Anand, S. V., Rajendran, D., Biswas, P. C., Omonov, S., & Ashfaq, R. (2026). Quantum machine learning models: Principles, frameworks, and computational challenges. In S. B. Khan, S. Khullar, M. A. Khan, U. Mamodiya, & M. L. Joshi (Eds.) Emerging Hybrid Models for Neuromorphic AI and Quantum Computing (pp. 137-168). IGI Global Scientific Publishing. https://doi.org/10.4018/979-8-3373-7779-7.ch005

Repository Citation

Jayabalaji, K. A., Anand, S. V., Rajendran, D., Biswas, P. C., Omonov, S., & Ashfaq, R. (2026). Quantum machine learning models: Principles, frameworks, and computational challenges. In S. B. Khan, S. Khullar, M. A. Khan, U. Mamodiya, & M. L. Joshi (Eds.) Emerging Hybrid Models for Neuromorphic AI and Quantum Computing (pp. 137-168). IGI Global Scientific Publishing. https://doi.org/10.4018/979-8-3373-7779-7.ch005

ORCID

0000-0001-8530-7850 (Rejendran)