Dynamic Topology for Quantum Machine Learning Optimization

Jincheng Zhang

Published Sep 5, 2026• 0 citations

Abstract

Quantum machine learning (QML) holds immense promise for revolutionizing various fields, including drug discovery, materials science, and artificial intelligence. However, training complex QML models often requires significant computational resources and careful tuning of hyperparameters. This paper proposes a novel dynamic topology optimization framework for QML, aiming to automate the parameter optimization process and enhance model generalization. The framework utilizes a dynamic representation of the model's quantum state, dynamically adjusting the topology of the quantum system to guide the optimization towards optimal configurations. This approach contrasts with traditional methods that rely on manual parameter adjustment, offering a potential pathway towards more efficient and robust QML algorithms. This work introduces a novel mathematical model and a practical implementation demonstrating the framework's ability to improve the performance of quantum machine learning models across various tasks.

DOI: 10.5281/zenodo.22321504

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