基于动态自适应的量子计算的自适应几何优化

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

This paper introduces a novel quantum computing framework, termed "Dynamic Adaptive Geometry Optimization" (DAGO), that leverages dynamic self-adaptation to optimize quantum circuits. Traditional quantum algorithms often rely on static geometric structures, limiting their adaptability to complex input data. DAGO dynamically adjusts the computation's geometry, allowing for significantly enhanced computational efficiency through automated geometric restructuring. This paper details the core mechanism of the framework, its potential benefits, and discusses its impact on the landscape of quantum computing.

DOI: 10.5281/zenodo.22186650

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