基于自适应自律的量子计算算法优化

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

Quantum computing holds immense promise for revolutionizing various fields, and efficient algorithm design is crucial for realizing this potential. However, current quantum algorithms often struggle with optimal parameter settings, limiting their performance. This paper introduces a novel quantum computing algorithm optimization method, leveraging adaptive self-regulation to dynamically adjust quantum computation parameters based on the specific characteristics of the quantum system. We demonstrate the effectiveness of this approach through a series of simulations and analysis, showcasing a significant improvement in algorithm efficiency and precision compared to traditional optimization techniques. The core mechanism focuses on establishing a self-regulating feedback loop that adapts the quantum parameters in real-time, enhancing the algorithm's ability to navigate complex quantum states. This approach offers a more flexible and powerful method for optimizing quantum algorithms, paving the way for enhanced computational capabilities.

DOI: 10.5281/zenodo.22183719

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