Quantum Algorithm Adaptive Complexity Adjustment Strategy

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

This paper explores the development of a novel quantum algorithm designed to dynamically adjust computational complexity during the execution of quantum computations. Traditional quantum algorithms often employ fixed complexity, limiting their applicability to specific datasets. Our approach aims to mitigate this limitation by automatically adapting the computational steps based on data characteristics, thereby enhancing performance across a range of inputs. The core concept revolves around an algorithm that analyzes data and automatically adjusts the quantum operations to achieve optimal results. We detail the proposed algorithm's architecture, the underlying principles driving its adaptive behavior, and preliminary experimental results demonstrating its effectiveness. The goal is to create a quantum algorithm that is inherently more versatile and efficient, capable of tackling a broader spectrum of computational problems.

DOI: 10.5281/zenodo.22199807

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