[Quantum theory of vision].

Maarten Anne Bouman

John Joly

M. H. Pirenne

Huang Pinga

The retina and cortex are correlated by the identical particle model.The vision system is modeled as identical particle system,and its radiation transition is studied by using time quantum theory of radiation-matter interaction.The identical particles form coherent states,the transition probability of the superradiant state is a liner function of N2 and N3(N is the particle number),and the one of the subradiant state is zero,which is supported by its successful primary explanation of the coherence of vision cells and pigments,respectively,and the co-action of retina and cortex in the vision adaptation of darkness and brightness.Identical particle model might hold for vision adaptation,and its further integration with Retinex theory will give a clue for the further research of vision adaptation.

Kumar Nikhil

The Quantum theory visualizes that uncertainty is the ultimate truth behind the dualism of wave and particle at the sub-atomic plane of Matter. It does not find any physical law capable of explaining the physical state in which the conversion of wave into particle and vice-versa takes place at the sub-atomic plane of Matter. In fact, the conversion evades all the physical laws on account of which Werner Heisenberg propounds his ‘Uncertainty Principle’. Though, the scientific world rejects Albert Einstein’s scientific solution in which he seeks to establish that uncertainty is not the truth, Sri Aurobindo’s vision of wave and particle as the forms of the ‘electric Energy’ in his epic poem ‘Savitri’, and “The Life Divine” proves Heisenberg’s vision of uncertainty to be untrue. The present paper seeks to establish the aforesaid vision of Sri Aurobindo in a scientific and logical manner. It goes on to further explain his higher vision that consciousness becomes ‘the electron, the atom and the material object’, the vision which comes into his being on a very high plane of yogic consciousness. Key words: Quantum theory, theory of relativity, spiritual consciousness, supramental consciousness.

Miss Sabiya Begum, Lubna Nausheen, Miss Ruqaiya Fatima

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. INTRODUCTIONObject recognition has become one of the most critical applications in the field of computer vision, with wide-ranging use cases in autonomous systems, medical imaging, surveillance, and human-computer interaction.Traditional deep learning models, particularly Convolutional Neural Networks (CNNs), have achieved remarkable progress in recognizing visual patterns.However, these models often rely solely on spatial feature extraction and fail to capture deeper contextual and representational information, which limits their effectiveness in handling complex and high-dimensional datasets.As object recognition tasks continue to grow in complexity, there is an increasing demand for models that combine efficiency with richer feature representations.Quantum-inspired approaches such as Quantum Vision (QV) have emerged to address this gap by introducing the concept of particle-wave duality from quantum physics into image recognition tasks.By treating objects as wave functions rather than fixed pixel intensities, QV enables models to capture more abstract and holistic representations of images.Although the integration of QV with CNNs has shown promise, these systems still face challenges, such as high computational costs, limited scalability, and inefficiency in extracting deeper hierarchical features.Hence, there is a need for architectures that can combine the representational strength of QV with modern, efficient deep learning frameworks.To overcome these challenges, this project introduces the Heavy QV-Xception model, which integrates a robust Quantum Vision block with the Xception architecture.The Xception model, based on depthwise separable convolutions, provides computational efficiency and high accuracy, while the Heavy QV block enriches image representations by transforming them into wave functions.This hybrid system leverages the advantages of both approaches, enabling scalable, efficient, and highly accurate object recognition.With extensive evaluation on benchmark datasets, the proposed model demonstrates superior performance compared to conventional CNNs and QV-CNN hybrids, marking a significant advancement in the field of deep learning-based object recognition. STIMULATINGThe scope of this project is to design and implement a Heavy QV-Xception model that integrates Quantum Vision (QV) theory with the Xception deep learning framework for advanced object recognition tasks.Unlike traditional CNN-based systems, which focus primarily on spatial pixel features, this model captures wave-function representations of objects, enabling a deeper and more holistic understanding of image data.By incorporating QV blocks, the model introduces quantum-inspired representational learning, which enhances the robustness of feature extraction across varying datasets and environmental conditions.This project is applicable to a wide range of real-world domains, such as autonomous driving, medical