Quantum-Inspired Data Structures for Parallel Processing

Jincheng Zhang

Published Sep 3, 2026• 0 citations

Abstract

This paper explores the potential of quantum-inspired data structures to revolutionize parallel processing, specifically focusing on design principles that mimic the phenomena of quantum entanglement. The core claim is that by leveraging data structures that inherently exploit these principles, we can significantly enhance the efficiency of parallel computations, particularly for large datasets. We investigate several promising data structure types – quantum walks and entanglement graphs – and analyze their potential to optimize data access and communication in parallel environments. The paper details the theoretical underpinnings of these structures, proposes an optimization strategy, and discusses the challenges and future research directions for this emerging field.

DOI: 10.5281/zenodo.22280165

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