A HOLISTIC REVIEW OF EMERGING AI-AUGMENTED ALGORITHMIC PARADIGMS IN POST-MOORE COMPUTING ARCHITECTURES: EXPLORING THE CONVERGENCE OF NEUROMORPHIC ENGINEERING, QUANTUM ACCELERATION, AND BIO-INSPIRED OPTIMIZATION TECHNIQUES

The continuous downscaling trend dictated by Moore's Law has reached physical limitations, prompting the emergence of alternative computing paradigms. This paper provides a holistic review of AI-augmented algorithmic paradigms shaping post-Moore architectures, with a focus on the convergence of neuromorphic engineering, quantum acceleration, and bio-inspired optimization. By situating these technologies in an interdisciplinary context, the paper outlines how AI is catalyzing new computational frameworks with improved adaptability, energy efficiency, and problem-solving capacity. The landscape showcases accelerated hybridization of classical and non-classical methods, enabled by advances in AI modeling, hardware abstraction, and self-organizing systems.