High-Order Quantum State Space Learning with Adaptive Topology

Jincheng Zhang

Published Aug 29, 2026• 0 citations

Abstract

Quantum state space learning is a critical challenge in the development of quantum algorithms and systems. Traditional methods often lack the integration of topological information, hindering efficiency gains. This paper introduces a novel high-order quantum state space learning algorithm, integrating quantum topology information to optimize quantum system parameters and topological structure. The core mechanism leverages a tailored learning process guided by topological constraints, leading to improved performance and enhanced quantum computational capabilities. We present a comprehensive framework for dynamically adjusting the optimization process based on topological properties, resulting in a significant advancement in the efficiency of quantum state space exploration.

DOI: 10.5281/zenodo.22158880

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