The Adaptive Quantum Algorithm Probability Model

Jincheng Zhang

Published Sep 2, 2026• 0 citations

Abstract

This paper introduces an adaptive quantum algorithm probability model, designed to enhance the optimization and stability of quantum algorithms. Traditional quantum algorithms often rely on static parameters, while this research focuses on probabilistic models, enabling efficient parameter tuning and improved performance. We present a framework for constructing probabilistic models that translate quantum algorithm execution into distribution functions, leveraging the inherent properties of probability to guide optimization. The core mechanism centers around exploiting quantum algorithms' unique characteristics to create a probabilistic representation of the computation, enabling targeted parameter adjustments to improve algorithm performance. The novel contribution lies in the integration of this probabilistic model into a robust optimization framework, addressing a critical challenge in the design and application of quantum algorithms.

DOI: 10.5281/zenodo.22248264

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