Sabiya Begum, Lubna Nausheen, Ruqaiya Fatima 03 08 In this work we extend the recently proposed Quantum Vision QV theory in deep learning for object recognition by integrating it with the Xception architecture forming a novel Heavy QVXception model The QV theory inspired by the particlewave duality in quantum physics treats objects as information waves rather than static images enabling deep neural networks to capture richer representations Building on this concept our Heavy QVXception model leverages a robust QV block to transform conventional images into wavefunction representations and processes them through the depthwise separable convolutional layers of Xception for enhanced feature extraction This hybrid approach benefits from both the quantuminspired information representation and the efficient highperformance architecture of Xception Extensive experiments on multiple benchmark datasets demonstrate that the Heavy QVXception model consistently outperforms standard Xception and other conventional CNNs highlighting the effectiveness of combining QV theory with advanced deep learning architectures for improved object recognition accuracy Copyright (c) Journal of Science Engineering Technology and Management Science. All rights reserved 2026