Quantum Computing Algorithm Learning for Adaptive Algorithms

Jincheng Zhang

Published Sep 3, 2026• 0 citations

Abstract

This paper introduces a novel quantum computing algorithm learning framework designed to dynamically adjust algorithm parameters based on training data characteristics. Traditional algorithm learning often relies on manual parameter tuning, limiting adaptability and potentially hindering performance. We propose a quantum-enhanced approach that leverages quantum algorithms to optimize parameters, thereby reducing computational complexity and enhancing algorithm efficiency. The framework is grounded in the principle of self-adaptation, allowing the algorithm to continuously refine its parameters to achieve optimal results. This work demonstrates the potential of quantum computing to significantly improve the performance of adaptive algorithms through dynamic parameter optimization.

DOI: 10.5281/zenodo.22281116

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