Evaluation of the Neural Quantum Theory in Vision

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

Shuteng Wang, Christian Theobalt, Vladislav Golyanik

M. H. Pirenne

Mahdi Seyfipoor, Mohammad Javad Samii Zafarqandi, Siamak Mohammadi

This paper presents an evaluation of various quantum algorithms for their potential application in real-time vision applications. By analyzing the underlying principles and mechanisms of key quantum algorithms, including those based on the Quantum Fourier Transform (QFT), amplitude amplification, quantum walks, and quantum optimization, the study aims to assess their suitability for enhancing vision tasks. The computational advantages offered by these quantum algorithms, such as speed and efficiency, are highlighted, particularly in comparison to classical approaches. Algorithms based on amplitude amplification, like Grover’s Algorithm and Quantum Counting, demonstrate significant promise for improving the accuracy and speed of object detection, especially in scenarios requiring real-time processing. Similarly, quantum walk algorithms show potential in navigating structured data efficiently, making them relevant for rapid detection tasks. The paper further explores the integration of quantum algorithms with classical techniques, emphasizing the development of hybrid models, such as Quantum Convolutional Neural Networks (QCNNs). Experimental comparisons between traditional Convolutional Neural Networks (CNNs) and QCNNs reveal that quantum-enhanced models may offer advantages in initial training phases, suggesting enhanced capabilities in feature extraction and pattern recognition. The results indicate that while quantum algorithms hold considerable promise for advancing the field of object detection, particularly in real-time applications, further research is needed to fully realize their potential. This study contributes to the growing field of quantum computing by providing a detailed analysis of how various quantum algorithms can be applied to complex data processing tasks in computer vision, offering new directions for the development of more efficient detection systems.

Senthilkumar Vijayakumar, Filious Louis, Shaunak Pai Kane +1 more

In this industry-specific research topic, we foresee creating a system that would deliver a 360° perspective of non-verbal and behavioural aspects into insightful contexts by recording kinesics like facial expressions, emotions, gestures, and spatial attributes throughout interview/meeting room processes conducted digitally or in person.In this research, we investigate the transfer learning training accuracy and performance measures of various pre-trained Computer Vision model architectures with newly devised hybrid end-to-end Quantum Neural Network (QNN) and evaluate hybrid QNN model inference performance for Computer Vision applications deployed in the real world.

Sitakanta Nayak, Shaktikanta Nayak, J. P. Singh