Topological Information Graphs for Quantum Algorithm Optimization

Jincheng Zhang

Published Sep 1, 2026• 0 citations

Abstract

Quantum computing promises revolutionary advancements across numerous fields, but realizing this potential faces significant challenges, primarily related to algorithm design and optimization. Traditional approaches often rely on static parameter settings, limiting the potential for performance gains. This paper introduces a novel quantum algorithm optimization strategy leveraging the power of topological information graphs. We propose a dynamic adjustment mechanism that iteratively refines the topology of the graph, allowing for adaptive optimization of quantum circuit parameters. We demonstrate the efficacy of this approach through extensive simulations and analyses, showcasing a significant improvement in quantum algorithm performance compared to existing techniques. The core mechanism focuses on exploiting the inherent structure of the graph to guide the optimization process, resulting in enhanced efficiency. This research offers a fundamentally new perspective on algorithm optimization within the quantum realm, holding promise for accelerating the development of practical quantum solutions.

DOI: 10.5281/zenodo.22220725

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