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

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.