Quantum Topology: Adaptive Learning Algorithm

Jincheng Zhang

Published Sep 3, 2026• 0 citations

Abstract

Quantum topology, a burgeoning field of physics, offers a novel approach to information processing and computation, potentially revolutionizing machine learning. This paper introduces a novel adaptive learning algorithm predicated on the principles of quantum topology, aiming to enhance the learning and optimization capabilities of quantum machine learning models. The core of this algorithm centers around exploiting the inherent adaptability of quantum states – a property that allows for continuous adjustments in the system's dynamics – to dynamically control the learning process. We explore the design of a self-adaptive learning framework that leverages this adaptability to achieve improved efficiency and accuracy in training quantum neural networks. The resulting algorithm demonstrates potential benefits over traditional machine learning techniques, particularly in situations requiring nuanced optimization and rapid adaptation. This work presents a foundational investigation into the application of quantum topology for enhancing the learning process of quantum machine learning models.

DOI: 10.5281/zenodo.22278735

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