自适应拓扑场论的量子计算算法的优化

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

This paper investigates the optimization of quantum computation algorithms using a novel self-adaptive topology field theory (ATFT) approach. Traditional optimization methods often require manual adjustment, offering limited flexibility. This work proposes a dynamic optimization strategy leveraging quantum computing's inherent parallelism to automatically adjust the objective function, enabling efficient global optimization of complex geometric shapes. We present a theoretical framework for this optimization, demonstrating its potential to significantly improve algorithm performance compared to conventional techniques. The core mechanism centers around utilizing quantum entanglement and superposition to explore a vast solution space in parallel, facilitating a more robust and adaptable optimization process. This research explores the potential of this approach for tackling computationally challenging problems in quantum algorithm design.

DOI: 10.5281/zenodo.22200917

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