Adaptive Topological Structure Learning for Quantum Entanglement Modeling

Jincheng Zhang

Published Sep 6, 2026• 0 citations

Abstract

This paper introduces a novel quantum entanglement modeling framework leveraging adaptive topological structure learning. Traditional quantum measurement techniques often rely on static, pre-defined topologies, limiting the system's ability to precisely capture complex dynamics. We propose a dynamic topology network that automatically adjusts its structure based on measurement outcomes, enabling quantum information processing with self-optimization capabilities. The core mechanism involves constructing a quantum entanglement network that evolves in response to data, leading to improved measurement accuracy and enhanced computational power. This approach offers a fundamentally new path towards flexible and powerful quantum computation.

DOI: 10.5281/zenodo.22470388

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