This study provides a theoretical expansion of the recent Data Relativistic Uncertainty (DRU) framework by formalizing a physics-to-AI paradigm for image enhancement. By modeling images as probabilistic wave functions rather than deterministic states, the paradigm explicitly integrates wave-particle duality to illustrate the system flow of how DRU leverages the intrinsic physical uncertainty of light, a dimension requiring further theoretical discussion. Consequently, this paradigm provides a rigorous Explainable AI (XAI) approach that enhances the interpretability of how DRU mitigates illumination bias and maintains robustness against data noise.
This study provides a theoretical expansion of the recent Data Relativistic Uncertainty (DRU) framework by formalizing a physics-to-AI paradigm for image enhancement. By modeling images as probabilistic wave functions rather than deterministic states, the paradigm explicitly integrates wave-particle duality to illustrate the system flow of how DRU leverages the intrinsic physical uncertainty of light, a dimension requiring further theoretical discussion. Consequently, this paradigm provides a rigorous Explainable AI (XAI) approach that enhances the interpretability of how DRU mitigates illumination bias and maintains robustness against data noise.
In this work we extend the recently proposed Quantum Vision (QV) theory in deep learning for image classification by integrating it with the Xception architecture, forming a novel Heavy QV-Xception model. The QV theory, inspired by the particle–wave duality of quantum physics, treats objects as information waves rather than static images, enabling deep neural networks to capture richer representations. Building on this concept, the 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.Traditional convolutional neural networks process spatial images as rigid arrays of local pixel intensities, which restricts their ability to model long-range correlations and dynamic context, and leaves them vulnerable to degradation when the input is affected by noise, occlusion, illumination variation or compression artefacts. To overcome this limitation, the proposed framework implements a robust QV transformation block at the frontend of the network. This block treats images not as static surfaces but as dynamic information waves, mapping standard spatial inputs into wave-function representations through amplitude and phase encoding. This quantum-inspired conversion enables the model to encode complex, non-local statistical features and structural variations that are typically missed during standard localised pixel processing.Once the image is converted into a wave-function representation, the features are processed through the core pipeline of the Xception framework. The backbone relies on depthwise separable convolutions, which factorise standard convolutions by separating spatial channel-wise mapping from cross-channel pointwise correlations, reducing computational cost by approximately an order of magnitude and thereby creating the headroom within which the cost of the front-end transformation is absorbed.The model was trained and evaluated on a six-class natural scene dataset comprising buildings, forest, glacier, mountain, sea and street categories. It achieved a training accuracy of 92.44 per cent, a validation accuracy of 93.57 per cent and a test accuracy of 93.16 per cent, correctly classifying 2804 of 3010 held-out images, with a weighted F1-score of 93.11 per cent. Analysis of the confusion matrix shows that classification errors are concentrated among visually related category pairs rather than distributed arbitrarily, and that the forest class achieved perfect recall. The trained model was deployed within a Flask web application providing authentication, image upload and real-time classification, demonstrating that the proposed architecture is both accurate and practically deployable.
The increasing deployment of Artificial Intelligence (AI) systems across various domains necessitates a deeper understanding of their decision-making processes. Current explainable AI (XAI) techniques often struggle to provide truly transparent and reliable explanations, particularly for complex models like deep neural networks. This paper proposes a novel approach to algorithmic transparency and XAI leveraging concepts from quantum information theory. Specifically, we explore the potential of quantum entanglement and mutual information to represent and analyze AI model decisions in a fundamentally different way. Our framework represents model outputs as quantum states and investigates the correlations between these states and the input data. This analysis aims to identify the key factors driving the model's decision and provide a more intuitive and understandable explanation. We demonstrate that utilizing quantum information theory can offer a richer and more robust approach to XAI than traditional methods, potentially addressing limitations related to interpretability and trust in AI systems. This work lays the groundwork for a new paradigm in AI transparency, utilizing the unique properties of quantum mechanics to enhance the explainability of complex algorithms.
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
The rapid advancement of Artificial Intelligence (AI) has heightened concerns about the opacity of decision-making processes in complex models, particularly deep neural networks. This research explores a novel paradigm that integrates quantum computing principles into Explainable AI (XAI) to enhance interpretability without compromising predictive accuracy. The study introduces a Quantum-inspired Interpretable Model (QIIM) framework that leverages Hilbert space embeddings, unitary transformations, and operator-based learning to represent knowledge in a mathematically transparent form. The proposed model computes interpretability through the expectation values of observable operators, enabling the decomposition of decisions into quantifiable, human-understandable components. To capture hierarchical relationships, a Tensor Network Interpretable Model (TNIM) is further developed, offering scalable insights into complex dependencies among features. Experimental evaluations—performed on benchmark datasets for decision-making tasks—demonstrate that the quantum-inspired models achieve competitive accuracy while significantly improving local and global interpretability metrics compared to classical XAI techniques. The findings underscore the potential of quantum formalism as a new foundation for transparent AI systems, bridging the gap between computational efficiency and explainability. This study contributes to both theoretical understanding and practical advancement in interpretable machine learning, paving the way for ethically aligned, transparent, and human-trustworthy AI-driven decision support systems.
The rapid growth of ultra-large and highdimensional datasets has intensified the demand for deep learning (DL) models that are scalable, accurate, transparent, and interpretable. In parallel, quantum-inspired computing has emerged as a promising paradigm that emulates quantum principles such as superposition, entanglement, and probabilistic state evolution without requiring quantum hardware. This study presents a comprehensive survey of explainable quantum-inspired DL models for large-scale data mining. Existing quantum-inspired architectures integrated with deep neural networks (DNN), including convolutional, recurrent, and attention-based models, are systematically analyzed alongside explainable artificial intelligence (XAI) techniques to enhance interpretability and trustworthiness. The reviewed methods are classified based on learning paradigms, explainability mechanisms, scalability strategies, and application domains such as healthcare analytics, cybersecurity, intelligent transportation, and industrial big data systems. Furthermore, evaluation metrics, computational complexity, and performance trade-offs are examined in comparison with conventional DL approaches. Finally, key open challenges and future research directions for real-world large-scale deployment are highlighted.
Quantum Artificial Intelligence (Quantum AI) embodies the integration of quantum computational paradigms with artificial intelligence frameworks to address the computational intractability and scalability constraints inherent in classical AI systems. By leveraging quantum mechanical properties such as superposition, entanglement, and quantum interference, Quantum AI enables enhanced representation learning, accelerated optimization, and improved probabilistic inference in high-dimensional problem spaces. In parallel, deep-tech innovations—including advanced deep learning architectures, neuromorphic computing, and edge-based intelligent processing—are redefining the architectural foundations of next- generation intelligent systems through energy-efficient, adaptive, and real-time computation. This paper presents a technically rigorous and comprehensive review of Quantum AI and deep-tech innovations, focusing on algorithmic formulations, architectural models, and system-level integration strategies. Core methodologies such as quantum machine learning algorithms, parameterized quantum circuits, variational quantum algorithms, and hybrid quantum–classical optimization frameworks are critically analysed with respect to computational complexity, repressibility, and feasibility on noisy intermediate-scale quantum (NISQ) hardware. Additionally, the role of neuromorphic computing in enabling spiking neural architectures and event-driven processing is examined as a complementary paradigm to quantum-enhanced learning models, particularly for low-power and latency-critical applications. The paper further investigates application- driven deployments of Quantum AI across domains including medical diagnostics, cryptographic security, autonomous decision-making, and smart cyber-physical systems, highlighting performance trade-offs and implementation constraints. Key challenges such as quantum noise, decoherence, data encoding bottlenecks, limited qubit connectivity, and ethical governance are systematically discussed. Finally, future research directions are identified, emphasizing fault-tolerant quantum learning architectures, scalable hybrid integration with deep-tech systems, and co-design methodologies for realizing practical next-generation intelligence platforms.