Quantum Entanglement-Based Continuous Learning Model (QCLM)

Jincheng Zhang

Published Sep 4, 2026• 0 citations

Abstract

This paper proposes a novel Continuous Learning Model (QCLM) leveraging the unique properties of quantum entanglement to address the challenges inherent in continuous learning scenarios involving multiple, interconnected quantum data sources. The core idea is to establish an entanglement network between these data sources, allowing for the extraction of information and the continuous, high-precision modeling of the overall system state. Unlike traditional machine learning approaches that rely on classical data and discrete training steps, QCLM exploits the non-classical nature of quantum entanglement to facilitate information fusion across data sources and achieve superior performance, particularly in high-dimensional, non-linear, and highly correlated quantum datasets. The model continuously updates its quantum state representation and utilizes the entanglement network for dynamic information integration, resulting in robust and adaptive learning capabilities. Mathematical formulations detail the key components of the QCLM, including the entanglement network structure, the quantum state representation, and the update rules governing the learning process. This approach offers a promising pathway for developing advanced quantum learning systems capable of handling complex, dynamic environments. ---

DOI: 10.5281/zenodo.22293960

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