基于量子态的自适应机器学习框架

Jincheng Zhang

Published Sep 6, 2026• 0 citations

Abstract

This paper introduces a novel quantum-enhanced machine learning framework leveraging the unique properties of quantum states. Traditional machine learning models often struggle with data distribution shifts and high dimensionality, hindering generalization and computational efficiency. We propose a framework that utilizes quantum states – specifically, superposition and entanglement – to improve model expressiveness and reduce computational complexity. The core mechanism involves a hybrid approach, integrating the classical machine learning model with a quantum-induced transformation of the input data. This integration allows the model to explore a wider range of potential solutions and efficiently handle complex relationships within the data. We demonstrate the framework's efficacy through simulations and preliminary experiments, showcasing significant improvements in model performance and resource utilization compared to conventional machine learning techniques. This research explores the potential of quantum mechanics to augment classical algorithms, opening avenues for more robust and efficient machine learning solutions.

DOI: 10.5281/zenodo.22447879

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