Integrating Quantum Vision Theory with Deep Learning for Enhanced Object Recognition Using Heavy QV-Xception

Abstract

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 QV-Xception model. The QV theory, inspired by the particle-wave 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 QV-Xception model leverages a robust QV block to transform conventional images into wave-function representations and processes them through the depthwise separable convolutional layers of Xception for enhanced feature extraction. This hybrid approach benefits from both the quantum-inspired information representation and the efficient, high-performance architecture of Xception. Extensive experiments on multiple benchmark datasets demonstrate that the Heavy QV-Xception 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.