Dynamic Programming with Quantum Memory Access Optimization

Jincheng Zhang

Published Aug 29, 2026• 0 citations

Abstract

This paper explores the potential of integrating quantum-inspired techniques into dynamic programming (DP) algorithms to optimize memory access patterns. Traditional DP relies heavily on sequential access to memory, which can become a significant bottleneck, particularly for large datasets. We propose a framework that utilizes concepts analogous to superposition and entanglement to parallelize memory access operations, thereby reducing computational overhead. The core claim is that by intelligently mimicking quantum memory access strategies, we can achieve speedups exceeding those attainable through purely classical DP approaches. We detail a methodology for adapting DP formulations to exploit this parallel access, focusing on techniques for efficiently managing and accessing memory locations. This work represents a novel application of quantum-inspired ideas to a well-established algorithmic paradigm, offering a promising avenue for enhancing the performance of DP in resource-constrained environments.

DOI: 10.5281/zenodo.22154658

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