自适应量子计算的拓扑优化

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

This paper introduces a novel framework for quantum computation optimization leveraging adaptive topology control. Traditional optimization methods often rely on pre-defined parameters, limiting the adaptability of quantum algorithms. This work proposes a dynamic topology optimization module that learns and adjusts the quantum system's topology during computation, enabling automated optimization of quantum algorithms, particularly for complex topological problems. We demonstrate the effectiveness of this approach through simulations and analysis of several challenging quantum algorithms. The core claim is that this framework enables significantly improved efficiency and accuracy compared to traditional optimization techniques in these scenarios.

DOI: 10.5281/zenodo.22201182

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