The entanglement properties of quantum states associated with directed graphs are investigated. Using a measure derived from the Fubini–Study metric, multipartite entanglement is quantitatively related to the local connectivity of the graph. In Entanglement in Directed Graph States (2025), arXiv:2505.10716, it is demonstrated that the vertex degree distribution fully determines this entanglement measure and remains invariant under vertex relabeling, highlighting its topological character. As a consequence, the measure depends only on the total degree of each vertex, making it independent of the distinction between incoming and outgoing edges. This framework is applied to several specific graph structures, including hierarchical networks, neural network–inspired graphs, full binary tree and linear bridged cycle graphs, demonstrating how their combinatorial properties influence entanglement distribution. These results provide a geometric perspective on quantum correlations in complex systems, offering potential applications in the design and analysis of quantum networks.
De Simone, L., Franzosi, R. (2025). Entanglement in Quantum Systems Based on Directed Graphs. ADVANCED QUANTUM TECHNOLOGIES [10.1002/qute.202500514].
Entanglement in Quantum Systems Based on Directed Graphs
De Simone, Lucio;Franzosi, Roberto
2025-01-01
Abstract
The entanglement properties of quantum states associated with directed graphs are investigated. Using a measure derived from the Fubini–Study metric, multipartite entanglement is quantitatively related to the local connectivity of the graph. In Entanglement in Directed Graph States (2025), arXiv:2505.10716, it is demonstrated that the vertex degree distribution fully determines this entanglement measure and remains invariant under vertex relabeling, highlighting its topological character. As a consequence, the measure depends only on the total degree of each vertex, making it independent of the distinction between incoming and outgoing edges. This framework is applied to several specific graph structures, including hierarchical networks, neural network–inspired graphs, full binary tree and linear bridged cycle graphs, demonstrating how their combinatorial properties influence entanglement distribution. These results provide a geometric perspective on quantum correlations in complex systems, offering potential applications in the design and analysis of quantum networks.| File | Dimensione | Formato | |
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https://hdl.handle.net/11365/1301096
