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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM64788
Volume:
10 |
6 |
Jun 2026
Pages:
1-15
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
physics
RV Institute of Technology , Chebrolu
Mathematics
RV Institute of Technology , Chebrolu
CSE
RV Institute of Technology , Chebrolu
ECE
RV Institute of Technology , Chebrolu
CSE
RV Institute of Technology , Chebrolu
Mathematics
RV Institute of Technology , Chebrolu
CSE
RV Institute of Technology , Chebrolu
The rapid advancement of humanoid intelligence systems has created an increasing demand for intelligent, transparent, and computationally efficient decision-making frameworks capable of operating in complex real-world environments. Conventional deep learning-based humanoid architectures often suffer from high computational complexity, limited interpretability, scalability constraints, and the black-box nature of autonomous decisions. Simultaneously, Quantum Machine Learning (QML) has emerged as a transformative paradigm that exploits quantum superposition, entanglement, and parallel computation to enhance learning efficiency and optimization capabilities. This paper proposes an Explainable Hybrid Quantum–Classical Deep Learning Framework for Intelligent Humanoid Systems (EHQCDL-IHS) that integrates Quantum Neural Networks (QNNs), Variational Quantum Circuits (VQCs), classical deep learning models, and Explainable Artificial Intelligence (XAI) mechanisms into a unified decision-making architecture. The proposed framework employs quantum-enhanced feature representation, hybrid optimization strategies, and explainability modules to improve prediction accuracy, transparency, adaptability, and robustness in Noisy Intermediate-Scale Quantum (NISQ) environments. Furthermore, SHAP- and LIME-based explanation modules provide interpretable insights into humanoid perception, reasoning, and action-selection processes. Experimental evaluation demonstrates superior performance compared with conventional Artificial Neural Networks (ANNs), Convolutional Neural Networks (CNNs), and standalone Quantum Neural Networks (QNNs), achieving an accuracy of 98.8%, precision of 98.6%, recall of 98.7%, and F1-score of 98.65%. The proposed framework provides a scalable and trustworthy foundation for next-generation humanoid intelligence systems in healthcare, autonomous robotics, smart manufacturing, defense, and smart city applications.
KEYWORDS
The proposed Explainable Hybrid Quantum–Classical Deep Learning Framework for Intelligent Humanoid Systems (EHQCDL-IHS) was evaluated to assess its effectiveness in intelligent decision-making, prediction accuracy, computational efficiency, and explainability. The framework integrates Deep Learning, Quantum Neural Networks (QNNs), Variational Quantum Circuits (VQCs), and Explainable Artificial Intelligence (XAI) mechanisms to enhance humanoid perception and autonomous reasoning. The performance of the proposed model was compared with conventional Artificial Neural Networks (ANNs), Convolutional Neural Networks (CNNs), Transformer Networks, and standalone Quantum Neural Networks (QNNs).
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Publication Date
6/11/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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Conflict of Interest
The authors declare no conflict of interest related to this research work.
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