基于量子自适应的动态隐马赛克算法优化

Jincheng Zhang

Published Sep 2, 2026• 0 citations

Abstract

This paper investigates the application of a novel quantum-adaptive dynamic hidden Markov model (DHMM) optimization algorithm designed to enhance the performance of various machine learning tasks, particularly those involving complex datasets. Traditional DHMM approaches often suffer from suboptimal parameter adjustment, limiting their effectiveness. We propose a quantum-driven dynamic adjustment of the DHMM's parameter space, allowing for automated optimization based on the input data's unique characteristics. This approach leverages quantum self-stabilization to iteratively refine the model's state, leading to improved accuracy, faster convergence, and enhanced robustness. The study demonstrates significant improvements in performance across a range of datasets, including image classification and natural language processing, validating the efficacy of this quantum-adaptive strategy.

DOI: 10.5281/zenodo.22255180

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