Robotics, Autonomous Vehicles, Data Science, and Quantum Neural Networks for Future Growth

Abstract

Robotics and autonomous vehicles (AVs) represent the vanguard of intelligent systems in manufacturing, logistics, and transportation. These domains are increasingly driven by advances in data science big data pipelines, real‐time analytics, and machine learning and by emerging quantum neural networks (QNNs) that promise exponential feature spaces and novel optimization capabilities. This article presents a comprehensive framework for integrating classical AI, data science methodologies, and QNN architecture in robotics and AVs. We review the state of the art in sensing, perception, control, and decision‐making; detail hybrid classical–quantum pipeline designs; and expound technical methods for QNN‐enhanced perception and planning. Three industry case studies illustrate how these convergent technologies accelerate throughput, increase safety, and enable new service models. We assess economic, workforce, and innovation impacts under a future‐growth lens, and discuss cybersecurity, ethical, and regulatory considerations. A prioritized research roadmap outlines near‐term hybrid pilots and long‐term fault‐tolerant quantum ambitions. By combining theoretical depth, technical rigor, and practical applications, this article equips researchers and practitioners to harness robotics, AVs, data science, and QNNs for sustainable industry transformation.