基于自适应量子势的机器学习算法

Jincheng Zhang

Published Sep 3, 2026• 0 citations

Abstract

This paper presents a novel machine learning algorithm based on adaptive quantum potentials. Traditional machine learning often relies on manual parameter tuning, which can be time-consuming and suboptimal. We propose a new approach where the quantum potential itself is dynamically adjusted during the training process, leading to improved model accuracy and enhanced quantum machine learning capabilities. The core mechanism involves a self-tuning quantum potential landscape, allowing the algorithm to automatically optimize the interaction between data and the quantum system. The algorithm's effectiveness is demonstrated through a series of benchmark datasets and analysis of its performance compared to existing methods. This work introduces a fundamentally new paradigm in quantum machine learning, offering a potential pathway to more efficient and accurate quantum computations.

DOI: 10.5281/zenodo.22278703

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