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The International Journal of Interdisciplinary Cultural Studies

A INTERPRETABLE QUANTUM-INSPIRED DEEP REINFORCEMENT LEARNING APPROACHES FOR LARGE-SCALE DATA ANALYTICS

G.S.Geethamani, D.Anitha

Published Aug 3, 2026• 0 citations
Publisher

Abstract

The rapid expansion of large-scale data analytics (LSDA) has presented significant issues concerning computing efficiency, scalability, adaptive learning, and model interpretability. In recent years, quantum-inspired deep reinforcement learning (QIDRL) has emerged as a promising research paradigm that integrates quantum-inspired optimization (QIO) concepts with deep reinforcement learning (DRL) approaches to enhance intelligent decision-making and complicated data processing. The need for transparent and reliable artificial intelligence (AI) systems has heightened the significance of interpretable learning frameworks that can elucidate model behavior and prediction results. This work offers a thorough examination of interpretable QIDRL methods for extensive data analysis. The review methodically examines current advancements in QIO models, interpretable RL architectures, policy explanation mechanisms, and scalable deep learning (DL) frameworks used for high-dimensional and dynamic datasets. The report examines the use of these methodologies across many sectors, including healthcare analytics, cybersecurity, smart transportation, industrial automation, financial forecasting, and intelligent Internet of Things (IoT) ecosystems. The study examines the benefits of QIDRL techniques in improving convergence speed, adaptive decision-making, feature optimization, and interpretability inside extensive analytical systems. Furthermore, significant research obstacles like computational complexity, training instability, scalability limitations, insufficient benchmark datasets, and the absence of defined criteria for interpretability assessment are thoroughly examined. The study emphasizes new research trends and prospects for creating robust, efficient, and transparent QIDRL frameworks for advanced intelligent DA applications.

DOI
10.18848/fjasbf06

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The rapid expansion of large-scale data analytics (LSDA) has presented significant issues concerning computing efficiency, scalability, adaptive learning, and model interpretability. In recent years, quantum-inspired deep reinforcement learning (QIDRL) has emerged as a promising research paradigm that integrates quantum-inspired optimization (QIO) concepts with deep reinforcement learning (DRL) approaches to enhance intelligent decision-making and complicated data processing. The need for transparent and reliable artificial intelligence (AI) systems has heightened the significance of interpretable learning frameworks that can elucidate model behavior and prediction results. This work offers a thorough examination of interpretable QIDRL methods for extensive data analysis. The review methodically examines current advancements in QIO models, interpretable RL architectures, policy explanation mechanisms, and scalable deep learning (DL) frameworks used for high-dimensional and dynamic datasets. The report examines the use of these methodologies across many sectors, including healthcare analytics, cybersecurity, smart transportation, industrial automation, financial forecasting, and intelligent Internet of Things (IoT) ecosystems. The study examines the benefits of QIDRL techniques in improving convergence speed, adaptive decision-making, feature optimization, and interpretability inside extensive analytical systems. Furthermore, significant research obstacles like computational complexity, training instability, scalability limitations, insufficient benchmark datasets, and the absence of defined criteria for interpretability assessment are thoroughly examined. The study emphasizes new research trends and prospects for creating robust, efficient, and transparent QIDRL frameworks for advanced intelligent DA applications.
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.
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.
Abstract: Quantum deep reinforcement learning is an integration of the principles of quantum computing with deep reinforcement learning in an effort to advance robot navigation tasks. Due to the explosion of the state and action spaces, traditionally DRL methods are limited in scalability and efficiency. QDRL, however, uses quantum superposition and entanglement for more efficient processing of big amounts of information-it enables robots to learn optimal policies for navigating complex environments. We introduce here a new QDRL framework in which quantum neural networks encode policy functions and value estimates. We demonstrate how quantum circuits can take advantage of multi-dimensional state representations in order to strengthen the power of a robot in changing its environment over time. Benchmark experiments on navigation tasks reflect phenomenal acceleration in learning performance along with better decision-making accuracy compared to classical DRL methods. We then consider integrating such quantum algorithms, as Grover's search, to improve exploration strategies in sparse reward settings. The experiments show that QDRL speeds up convergence rates and provides robots with the possibility to react more rapidly to unexpected obstacles. It discusses new applications for quantum computing of autonomous systems, potentially applied to robotics, autonomous vehicles, and other environments which require complex decision making. Some lines of work in the future are optimization of the resources needed for quantum computers and hybrid architectures for the resolution of classical problems in navigation
The significant rise in wireless communication, driven by sixth-generation (6G) networks, underscores the urgent need for innovative resource allocation methods to achieve efficiency, scalability, and reliability. This survey explores the integration of Quantum Deep Reinforcement Learning (QDRL) as a strategy to address the challenges posed by dynamic and complex wireless environments. By leveraging quantum computing features such as superposition and entanglement, QDRL enhances computational efficiency and mitigates the scalability limitations of classical and deep reinforcement learning (DRL) in high-dimensional optimization. The study reviews recent QDRL approaches for key resource allocation tasks, including power distribution, user scheduling, and channel assignment, and examines quantum algorithms such as the Quantum Approximate Optimization Algorithm (QAOA) and Quantum Neural Networks (QNN) for improved decision-making in wireless systems. Furthermore, it emphasizes the connection between quantum computing and DRL, highlighting hybrid architectures that balance computational cost, energy efficiency, and scalability. Finally, the survey identifies research gaps and outlines future directions, paving the way for robust QDRL strategies to support next-generation wireless networks.
The integration of Quantum-Inspired Reinforcement Learning (QiRL) into Edge-Driven CyberPhysical Systems (CPS) presents a transformative approach to enhance real-time decision-making in distributed environments. While classical reinforcement learning (RL) struggles with scalability and computational complexity in edge-CPS, quantum-inspired Quantum-Inspired Reinforcement Learning for Edge-Driven Cyber-Physical Systems algorithms leverage superposition and entanglement principles to optimize policy exploration and convergence. This paper proposes a novel QiRL framework that combines quantum-inspired optimization with deep RL, enabling efficient resource allocation and adaptive control in latency-sensitive applications (e.g., smart grids, autonomous drones). We formalize the mathematical foundations of QiRL, design a hybrid neural-quantum architecture for edge deployment, and evaluate its performance under dynamic CPS constraints. Simulations on IoT-based case studies demonstrate 20−35%\mathbf{2 0}-\mathbf{3 5 \%}20−35% faster convergence and 15%\mathbf{1 5 \%}15% higher energy efficiency compared to classical Deep Q-Networks (DQN). Key challenges, such as quantum noise simulation and edge-hardware limitations, are discussed to guide future research. This work bridges quantum computing principles with edge intelligence, offering a scalable pathway for nextgeneration CPS.
High-dimensional medical datasets pose a persistent challenge for artificial intelligence because traditional classification algorithms often incur escalating computational costs and reduced predictive accuracy. As healthcare systems generate increasingly complex clinical records, imaging outputs, and genomic profiles, scalable analytic methods that balance precision and efficiency are critical. This study proposes a Quantum-Inspired Optimization (QIO) framework for efficient and accurate classification of high-dimensional healthcare data. Leveraging the exploratory power of variational quantum algorithms, specifically techniques analogous to the Quantum Approximate Optimization Algorithm, the framework integrates quantum-style search strategies with classical computation to achieve global optimization and numerical stability. Publicly available medical datasets with hundreds of features were used to evaluate the approach. Classification models were trained and tested across varying feature dimensionalities, and performance was assessed using accuracy, runtime, and scalability metrics. Empirical results demonstrate that QIO achieves up to 95.4% classification accuracy and reduces computational time by 40% compared with state-of-the-art classical baselines. The method demonstrates stable convergence and clear decision boundaries even as feature dimensionality grows, highlighting its resilience to the curse of dimensionality. These results indicate that QIO can enable fast and reliable healthcare analytics in data-rich clinical environments. Future research may examine domain-specific adaptations, real-time deployment, and integration with emerging quantum hardware to enhance the impact of quantum-inspired artificial intelligence further.