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Jincheng Zhang
Abstract
This paper investigates the application of dynamic topological geometry to quantum algorithm expansion. We propose a novel quantum algorithm extension leveraging dynamically adjusted parameters within a simulated environment. The core mechanism centers around applying topological transformations during the simulation process, allowing the algorithm to adapt its parameters in real-time, thereby enhancing performance and efficiency. The paper details the design of the algorithm, its potential benefits, and a preliminary analysis of its effectiveness through simulated examples. We emphasize the significance of this approach for tackling complex optimization problems and highlight the advantages over traditional parameter tuning methods.