基于动态自适应的量子计算的自适应几何优化与自适应拓扑优化

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

This paper introduces a novel framework for quantum computation based on dynamic adaptive geometry optimization and adaptive topology optimization. Traditional quantum algorithms often rely on static, predetermined geometric structures and topology, which struggle to accommodate dynamic input data. Our proposed method leverages dynamic self-adaptation to automatically adjust the computation's geometry and topology, resulting in enhanced quantum computational efficiency. We demonstrate the effectiveness of this framework through theoretical analysis and experimental validation on a simplified quantum circuit problem. The core mechanism involves iterative refinement of the geometry and topology based on the input data, leading to improved performance compared to static approaches. This work represents a significant advancement in the design of quantum algorithms, specifically tailored for dynamic environments.

DOI: 10.5281/zenodo.22184408

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