Design and Analysis of Modern Quantum Neural Network Architectures for Intelligent Systems

Authors

• Lakshmi Chandrakanth Kasireddy, ThoughtSpot Inc, USA
• Prabhakara Rao Kapula, B.V. Raju Institute of Technology
• Dineshkumar Rajendran, Old Dominion UniversityFollow
• Neha Bharani, IPS Academy
• Srikanth Pulipeti, MPSTME SVKMs NMIMS University
• Islombek Khushvaktov, Tashkent State University of Economics

Document Type

Book Chapter

Publication Date

2026

DOI

10.4018/979-8-2600-0008-3.ch009

Publication Title

Hybrid AI Architectures for Intelligent Systems

Pages

253-286

Abstract

Quantum neural networks (QNNs) offer a principled pathway for integrating quantum computation with machine learning through superposition- and entanglement-based representations. This chapter proposes an architecture-aware design and evaluation framework for modern QNNs, emphasizing robustness and system feasibility alongside predictive performance. Multiple architectures variational QNNs, quantum convolutional neural networks, tensor-network hybrids, and fully quantum models—are assessed under a unified protocol. Experimental analysis shows that the proposed architecture-search–guided QNN achieves 91.8% classification accuracy and an F1-score of 0.914, outperforming fixed-template variational QNNs by approximately 5.6 percentage points. Under depolarizing noise with probability p = 0.10, the proposed model retains 85.3% accuracy, whereas baseline QNNs fall below 80%. Moreover, circuit depth is reduced by nearly 25% relative to standard variational designs, leading to faster convergence (42 epochs vs. 57 epochs).

Rights

© 2026 by IGI Global Scientific Publishing

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Original Publication Citation

Kasireddy, L. C., Kapula, P. R., Rajendran, D., Bharani, N., Pulipeti, S., & Khushvaktov, I. (2026). Design and analysis of modern quantum neural network architectures for intelligent systems. In S. B. Khan, S. Khullar, M. A. Khan, U. Mamodiya, & M. L. Joshi (Eds.), Hybrid AI architectures for intelligent systems (pp. 253-286). IGI Global Scientific Publishing. https://doi.org/10.4018/979-8-2600-0008-3.ch009

Repository Citation

Kasireddy, L. C., Kapula, P. R., Rajendran, D., Bharani, N., Pulipeti, S., & Khushvaktov, I. (2026). Design and analysis of modern quantum neural network architectures for intelligent systems. In S. B. Khan, S. Khullar, M. A. Khan, U. Mamodiya, & M. L. Joshi (Eds.), Hybrid AI architectures for intelligent systems (pp. 253-286). IGI Global Scientific Publishing. https://doi.org/10.4018/979-8-2600-0008-3.ch009

ORCID

0000-0001-8530-7850 (Rejendran)