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Quantum Entangled Graphs for Optimization

Jincheng Zhang

Published Sep 6, 2026• 0 citations
Publisher

Abstract

Quantum entangled graphs (QEG) offer a promising approach to quantum computation, leveraging the unique properties of entanglement to enhance algorithm efficiency and performance. This paper proposes a novel optimization algorithm, "Adaptive Quantum Process Optimization," (AQPO) designed to effectively optimize QEG's design. AQPO utilizes dynamic adjustment of quantum process parameters to control the output of the entangled graph, resulting in enhanced quantum computation performance. The core mechanism of the algorithm focuses on a continuous, iterative process of adjusting quantum process parameters to achieve optimal graph design. We present a theoretical framework and preliminary experimental results demonstrating the effectiveness of AQPO in optimizing QEG designs, highlighting its potential to significantly improve quantum computations.

DOI
10.5281/zenodo.22509415

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