NANO-SCALE ARCHITECTURES FOR QUANTUM AI ROBOTICS: A REVIEW OF EMERGING TRENDS IN NEUROMORPHIC CHIP DESIGN, ADVANCED MEDICAL DIAGNOSTICS, AND CYBERNETIC ENHANCEMENTS
Abstract
Quantum AI robotics, driven by the convergence of quantum computing, neuromorphic engineering, and artificial intelligence, is transforming domains such as medical diagnostics, autonomous robotics, and human augmentation. Traditional computing architectures face significant limitations in scalability, energy efficiency, and real-time adaptability challenges that quantum-enhanced, nano-scale neuromorphic systems are uniquely positioned to address. This study investigates the emerging role of nano-scale architectures in Quantum AI robotics by synthesizing recent developments in neuromorphic chip design, advanced diagnostic instrumentation, and cybernetic interfaces, using a structured literature review methodology guided by PRISMA protocols to identify and analyze peer-reviewed publications from 2018 to 2025 across databases such as IEEE Xplore, ScienceDirect, and SpringerLink. Key findings reveal that memristor-based crossbars, 3D-stacked neuromorphic chips, and quantum-assisted learning loops have enabled significant advancements in energy-efficient, real-time decision-making hardware; for example, a recent prototype combining quantum memristors with spiking neural networks achieved a 65% reduction in power consumption while maintaining computation speeds ten times faster than classical edge AI chips in robotic control tasks. In healthcare, nano-biosensors and quantum-enhanced imaging systems offer unprecedented diagnostic precision, while AI-driven brain-machine interfaces and prosthetics are revolutionizing cybernetic enhancement. The research further highlights open challenges in ethical governance, scalability, and security that must be addressed to fully realize the societal potential of Quantum AI robotics at the nano-scale.
Keywords : Cybernetics, Medical Research, Robotics, Quantum AI, Neuromorphic Chips, Nano-Scale Architectures.
