基于自适应的拓扑信息学习的量子计算算法

Jincheng Zhang

Published Sep 2, 2026• 0 citations

Abstract

This paper introduces a novel quantum computation algorithm based on self-adaptive topology learning. The algorithm dynamically adapts to the system's topology, enabling improved efficiency and stability compared to traditional approaches. We propose a mechanism for learning and utilizing topological information to optimize quantum circuit design and execution. The core of the algorithm involves a self-learning process that iteratively refines the quantum state representation based on observed topology. This iterative refinement leads to significant improvements in quantum circuit performance, particularly in terms of reduced error rates and enhanced coherence. The design incorporates a novel approach to represent and utilize topological information, resulting in a fundamentally different algorithm that can address challenges in quantum computation. The paper concludes with a detailed analysis of the algorithm's performance and discusses potential future directions.

DOI: 10.5281/zenodo.22247496

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