基于自适应的量子电路优化

Jincheng Zhang

Published Sep 7, 2026• 0 citations

Abstract

This paper introduces a novel approach to quantum circuit optimization based on adaptive parameter adjustment. Traditional optimization methods often rely on static parameters, struggling with complex and non-linear circuits. Our algorithm dynamically adjusts circuit parameters through a simulated computational process, enabling automated optimization and improved circuit performance. The core mechanism involves learning a mapping between circuit characteristics and resulting computations, allowing for continuous parameter refinement. This adaptive strategy is expected to significantly enhance the efficiency and robustness of quantum circuit design and execution.

DOI: 10.5281/zenodo.22637745

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