基于动态时空几何的量子算法优化

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

This paper introduces a novel quantum algorithm optimization approach based on dynamic time-space geometry. Traditional quantum algorithm optimization often relies on static parameter adjustments, limiting the potential for improved performance. We propose a framework wherein quantum algorithm parameters are represented as dynamic spacetime geometries, allowing for intelligent parameter optimization driven by the geometric properties of these spaces. This framework leverages the inherent flexibility of these geometries to enhance the efficiency of quantum computations. The core mechanism involves constructing a dynamic spacetime geometry for each quantum algorithm and subsequently optimizing the parameters within this geometry. We demonstrate the effectiveness of this method through theoretical analysis and preliminary experimental results, showcasing significant performance gains compared to conventional approaches. The paper concludes with a discussion of the potential implications and future directions for this approach.

DOI: 10.5281/zenodo.22186644

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