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.
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.
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.
This research paper explores the explainability of decision-making in hybrid quantum–classical classification models. Quantum Machine Learning (QML) combines the power of quantum computing with classical machine learning techniques to solve complex computational problems. However, understanding how these hybrid models make decisions remains a significant challenge. The study focuses on analyzing hybrid quantum–classical architectures and evaluating methods that improve the interpretability of their predictions. Techniques from Explainable Artificial Intelligence (XAI) are applied to investigate the internal decision-making processes of the models. The research also compares classical machine learning approaches with quantum-enhanced models to understand their performance and transparency. The results demonstrate that explainability techniques can help researchers better interpret model predictions, identify important features, and improve trust in hybrid quantum–classical systems. This work contributes to the development of transparent and reliable quantum machine learning applications in areas such as healthcare, finance, and data analytics.
Quantum circuit optimization is crucial for realizing the full potential of quantum computing, but current optimization techniques often operate as black boxes, lacking transparency and hindering trust. This paper proposes a novel approach combining tensor networks with explainable AI to address this challenge. We represent quantum circuits using tensor networks, allowing for a richer mathematical representation and facilitating the application of explainable AI methods. The developed system generates visualizations and textual explanations detailing the key factors driving optimization decisions, promoting understanding and trust in the process. The core claim highlights the critical need for explainability in AI-driven optimization, while the proposed mechanism leverages tensor networks to achieve this. We demonstrate the potential of this combined approach to enhance transparency and accelerate the adoption of quantum circuit optimization techniques. This work introduces a new paradigm for quantum optimization, moving beyond opaque algorithms towards interpretable and trustworthy solutions.
Artificial Intelligence (AI) has developed into a disruptive technology that now impacts multiple industries including healthcare and finance and education and scientific research. Recent developments in Generative Artificial Intelligence (GenAI) and Explainable Artificial Intelligence (XAI) and Agentic AI have enabled intelligent systems to operate beyond their typical predictive analytics functions. GenAI enables machines to produce original content which includes text and images and code through its extensive models and deep learning systems while XAI develops methods to make complex AI systems more understandable to users. Agentic AI improves the field by creating systems which can operate as independent units that make decisions based on their objectives and ongoing environmental changes. The current progress still faces major obstacles because the system needs to solve problems related to computing power and understanding models and expanding systems and making autonomous AI decisions dependable. This chapter investigates quantum computing which functions as a new computing method that can solve these specific problems. Quantum computing applies quantum parallelism and quantum optimization methods to achieve faster processing speeds for complex data and model training tasks which modern AI systems require. The combination of quantum computing with GenAI and XAI and Agentic AI will enhance model performance while creating better interpretability systems and developing stronger autonomous reasoning capabilities. The chapter shows how quantum-enhanced algorithms enable researchers to conduct large-scale data analysis while showing results more clearly and improving decision-making abilities of autonomous AI systems. The research demonstrates how advanced AI systems are combining with quantum technologies and establishes a new conceptual framework for future intelligent systems. The future of AI will transform through the combination of GenAI with XAI and Agentic AI and quantum computing which will create intelligent systems that operate with high autonomy while maintaining complete system transparency to solve complex real-world challenges.
In recent years, machine learning has achieved remarkable success in tasks such as image classification and solving complex real-world problems. The achievements of deep neural networks (DNNs) have led to the widespread adoption of block-based architectures. However, as the demand for Explainable AI (XAI) grows, there is a need for models that provide greater transparency and interpretability. Our work addresses this need by exploring the potential of quantum-classical hybrid models. By integrating classical neural networks (ClaNNs) with quantum neural networks (QNNs), we aim to develop AI systems that are not only powerful but also interpretable. We propose a framework that combines parameterized quantum circuits (PQCs) with classical layers to create a hybrid block design for QNNs. By adding a linear layer after the PQCs, the flexibility of the block design is increased. This architecture enhances the adaptability of QNN design and facilitates the integration of XAI principles through the comparison of the loss function planes. Our training method, inspired by knowledge distillation, addresses existing challenges in PQC-based QNN designs, providing insights into the optimization landscape. We validate the effectiveness of our hybrid block through experiments on standard datasets, demonstrating its potential to achieve comparable accuracy to traditional DNNs while providing improved interpretability.
Quantum computing presents a paradigm shift in computation, offering the potential to solve problems intractable for classical computers. However, the inherent complexity of quantum mechanics poses a significant challenge: the lack of interpretability. This paper introduces an Explainable Quantum Computing Model (EQCM), built upon the foundations of probability logic and information theory, designed to provide a transparent understanding of quantum computation processes and outcomes. The model aims to bridge the gap between the abstract nature of quantum algorithms and human intuition. We propose a framework where quantum operations are represented and interpreted within a probabilistic logic framework, allowing for the tracing and explanation of quantum state transformations. The core of the EQCM lies in quantifying uncertainty and leveraging information theory to derive explanations for observed quantum behaviors. This work contributes to the growing field of quantum explainability, paving the way for improved debugging, validation, and ultimately, wider adoption of quantum computing technologies. The model's key features include a probabilistic representation of quantum states, a formal method for tracking quantum operations, and a mechanism for quantifying the degree of uncertainty associated with quantum outcomes. We demonstrate how this framework can be applied to simple quantum algorithms, providing insights into their underlying mechanisms.