Quantum Data Science Models for Next-Generation Intelligent Computing Systems

Abstract

Quantum data science integrates quantum computing principles with data-driven intelligent systems, enabling unprecedented computational power for large-scale analytics, optimization, and machine learning. Next-generation intelligent computing systems are increasingly dependent on models capable of processing high-dimensional quantum states, exploiting superposition, entanglement, and non-classical probability distributions. This article examines emerging quantum data science models, their architectures, and their potential role in enabling ultra-efficient intelligent systems. It further discusses hybrid quantum–classical learning paradigms, scalability challenges, and future research trajectories. The findings emphasize the transformative impact of quantum models on computation-intensive tasks and outline pathways toward fully quantum-native intelligent systems.