Adaptive Topologies of Quantum Information Learning

Jincheng Zhang

Published Sep 2, 2026• 0 citations

Abstract

Quantum information learning is a rapidly evolving field with significant potential for breakthroughs in quantum computation and quantum information processing. Traditional approaches often rely on manually crafted quantum circuits and complex optimization strategies. This paper proposes a novel approach to quantum information learning based on adaptive topologies, leveraging the non-locality inherent in quantum entanglement. We present a new algorithm that utilizes a knowledge graph constructed from quantum entanglement to dynamically model quantum states, resulting in more precise physical simulations and numerical computations. The core mechanism involves constructing a topological knowledge graph to represent the quantum state, and then using the non-local properties of entanglement to learn and evolve this graph. This algorithm offers a promising path towards automated optimization and improved performance in quantum information learning.

DOI: 10.5281/zenodo.22251301

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