Self-Adaptive Quantum Algorithm Optimization

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

This paper presents a novel approach to quantum algorithm optimization leveraging self-adaptive quantum geometry optimization. The core idea is to dynamically adjust quantum algorithm parameters based on the inherent geometric properties of the quantum system being considered. This dynamic adjustment aims to enhance the precision of quantum computations, offering a significant advancement over static optimization techniques. We introduce a novel quantum geometry optimization framework that iteratively refines the algorithm parameters to achieve optimal performance. The resulting algorithm exhibits improved accuracy and robustness across a range of quantum systems. This work provides a foundation for more efficient and reliable quantum computation.

DOI: 10.5281/zenodo.22191429

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