Adaptive Machine Learning Framework based on Quantum Topology

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

This paper introduces an innovative machine learning framework, termed the Adaptive Machine Learning Framework (AMLF), predicated on the principles of quantum topology. The AMLF dynamically adapts its model architecture and parameters based on topological properties, aiming to enhance model accuracy and generalization capabilities. We explore the potential of leveraging entanglement and topological constraints to create a self-optimizing learning system. The core mechanism centers around incorporating quantum entanglement into the model's structure, allowing for autonomous parameter adjustment driven by topological principles. This approach offers a departure from traditional machine learning, potentially leading to improved performance and robustness. The paper details the theoretical framework, initial implementation, and preliminary results demonstrating the framework's capabilities.

DOI: 10.5281/zenodo.22188030

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