基于量子态的算法优化器

Jincheng Zhang

Published Sep 7, 2026• 0 citations

Abstract

This paper explores the potential of leveraging quantum mechanics to enhance algorithm optimization for machine learning models. Traditional optimization algorithms often struggle with complex, high-dimensional parameter spaces, and the inherent properties of quantum systems offer a promising avenue for improvement. We propose a novel algorithm, "Quantum Optimizer," that utilizes quantum state representations to dynamically adjust model parameters, leading to improved performance and efficiency. The core mechanism involves mapping model parameters to quantum states, applying quantum algorithms to manipulate these states, and then reconstructing the optimized model. This paper details the theoretical foundations of Quantum Optimizer, demonstrates its experimental results on a benchmark dataset, and discusses future research directions.

DOI: 10.5281/zenodo.22635971

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