High-Order Quantum Adaptive Algorithm Parameter Optimization Framework

Jincheng Zhang

Published Sep 3, 2026• 0 citations

Abstract

This paper introduces a novel high-order quantum adaptive algorithm parameter optimization framework. Traditional parameter optimization methods often rely on static adjustments, failing to fully leverage the dynamic behavior of quantum systems. Our framework dynamically adjusts quantum parameters based on real-time quantum computation results, offering a more effective optimization strategy. We propose a method that integrates optimization with quantum system dynamics, allowing for adaptive parameter tuning. This innovation aims to significantly enhance the performance of quantum algorithms by intelligently adjusting quantum parameters during the computation process. The proposed framework demonstrates improved results across a range of benchmark problems, showcasing its potential for advancing quantum computational capabilities.

DOI: 10.5281/zenodo.22274229

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