基于自适应的量子计算的拓扑结构优化

Jincheng Zhang

Published Aug 29, 2026• 0 citations

Abstract

Quantum computing offers the potential for revolutionary computational capabilities, but realizing this potential requires careful design of quantum circuits and algorithms. Quantum state optimization is a crucial step in this process, and traditional optimization methods often struggle with the inherent complexity of quantum systems. This paper presents a novel approach to quantum state optimization utilizing adaptive quantum state transformations, leveraging the self-adaptability of quantum states to achieve enhanced computational efficiency and precision. We detail a method that dynamically adjusts the quantum state topology to address specific challenges, resulting in improved performance compared to existing static optimization techniques. The core mechanism focuses on exploiting the quantum state's ability to evolve and adapt to changing conditions, thereby enabling the discovery of optimal quantum circuits. This work addresses the limitations of conventional optimization approaches by introducing a dynamic, self-adjusting process that directly interacts with the quantum system's inherent properties. The proposed method provides a promising path toward accelerating quantum algorithm development and enhancing the performance of various quantum computations.

DOI: 10.5281/zenodo.22157326

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