Quantum Graph-Based Parallel Algorithm Optimization

Jincheng Zhang

Published Sep 6, 2026• 0 citations

Abstract

Quantum Graph-Based Parallel Algorithm Optimization represents a novel approach to algorithm optimization leveraging the unique properties of quantum graphs. This research investigates a framework that transforms algorithms into quantum graphs, exploiting quantum computing's capabilities for enhanced parallel processing and improved performance. The core mechanism focuses on utilizing quantum properties to significantly accelerate algorithm execution. This work demonstrates the potential of quantum graph-based optimization to dramatically enhance algorithm efficiency compared to traditional methods. The study explores the design of a quantum graph representation for various algorithm types, focusing on the impact of quantum properties on parallelization and optimization. The resulting framework offers a promising avenue for advancing algorithm optimization techniques, potentially opening new possibilities for high-performance computing.

DOI: 10.5281/zenodo.22510623

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