Adaptive Memory Architectures for Quantum-Inspired Computing: A Framework for High-Dimensional Problem Solving

Abstract

This paper presents a novel hybrid memory architecture inspired by quantum computing principles—specifically entanglement and superposition—to enhance computational efficiency in addressing high-dimensional, NP-hard problems. The proposed framework introduces a tensorized memory lattice coupled with a dynamic memory routing protocol, both designed for implementation on classical computing hardware such as CPUs and GPUs. By emulating quantum behaviors within a classical context, the architecture facilitates accelerated problem-solving capabilities. Experimental evaluations on complex optimization tasks, including the Traveling Salesman Problem and protein folding simulations, demonstrate the framework's potential in achieving near-quantum performance levels. This approach offers a scalable pathway for integrating quantum-inspired methodologies into existing classical systems, paving the way for advancements in various computational domains.