Quantum Algorithms' Adaptive Optimization and Expansion

Jincheng Zhang

Published Sep 4, 2026• 0 citations

Abstract

Quantum algorithms hold immense potential for revolutionizing various fields, and a key challenge in their development lies in optimizing their performance across diverse input datasets. This paper proposes a novel approach to quantum algorithm optimization centered around a dynamically adaptive optimization framework. We introduce a quantum algorithm that adapts its parameter adjustments based on the characteristics of the input data, achieving improved efficiency and performance through real-time parameter tuning. This system, termed "Quantum Adaptive Tuning," leverages a combination of quantum optimization techniques with a dynamic parameter adjustment mechanism. The core mechanism is to iteratively refine the quantum algorithm's parameters to maximize the likelihood of finding optimal solutions. We present a theoretical analysis and initial experimental validation demonstrating the effectiveness of this approach. The paper concludes with a discussion of the implications of this work and future research directions.

DOI: 10.5281/zenodo.22291309

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