Core Claim: Develop a machine learning algorithm based on adaptive quantum topology field simulation, accelerating quantum machine learning algorithms and enhancing algorithm generalization.

Jincheng Zhang

Published Sep 3, 2026• 0 citations

Abstract

Quantum machine learning is a rapidly evolving field with the potential to revolutionize various computational tasks. This paper investigates the application of adaptive quantum topology field simulation as a novel approach to enhance quantum machine learning. We propose a machine learning algorithm that dynamically adjusts the number and state of quantum bits, leveraging the unique properties of quantum topology to accelerate training and improve generalization. The core mechanism centers around a system of optimized quantum circuit design coupled with adaptive quantum state management. The development of this algorithm aims to overcome limitations of traditional quantum machine learning algorithms, particularly in terms of computational complexity and adaptability. This paper presents a detailed theoretical analysis and preliminary experimental results demonstrating the effectiveness of this approach in a range of quantum machine learning tasks. The research explores the potential benefits of this method in terms of faster training times, improved model accuracy, and enhanced robustness across diverse datasets. Finally, we discuss the challenges and future directions for further investigation in this field.

DOI: 10.5281/zenodo.22263800

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