Title: Adaptive Complexity Optimization of Quantum Algorithms

Jincheng Zhang

Published Sep 2, 2026• 0 citations

Abstract

Quantum algorithms, renowned for their potential to solve complex problems beyond the capabilities of classical computers, often face challenges related to computational complexity. This paper introduces a novel approach to address this challenge – Adaptive Complexity Optimization of Quantum Algorithms (ACOA). We propose a method that dynamically adjusts the complexity of quantum algorithms based on the characteristics of the input data. The core mechanism centers around employing a topological quantum algorithm to decompose the algorithm's complexity into modular components, and automatically adjusting these modules based on the input's inherent structure. This adaptive approach aims to improve efficiency and scalability by effectively modeling complex data. The paper details the design of the OACO algorithm, its implementation, and preliminary experimental results demonstrating its effectiveness in tackling specific problem instances. We highlight the advantages of this method over traditional complexity optimization techniques, emphasizing its dynamic adaptability and potential for enhanced performance.

DOI: 10.5281/zenodo.22247980

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