Quantum Entangled Graphs for Adaptive Computation: A Novel Framework

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

Quantum Entangled Graphs (QEG) represent a promising paradigm for quantum computation, offering the potential for highly efficient and scalable algorithms. This paper explores the application of QEGs to adaptive computation, proposing a novel framework that dynamically adjusts quantum circuit parameters to optimize performance for diverse computational tasks. We outline the core mechanism for this adaptation, detailing a computational pipeline that leverages the inherent properties of QEGs to optimize gate choices and circuit structure. The potential benefits of this adaptive approach are significant, promising enhanced computational efficiency compared to static parameter adjustments. This work addresses a critical challenge in the development of practical quantum algorithms and highlights a new direction for exploring the application of QEGs to computational problems.

DOI: 10.5281/zenodo.22205456

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