A Resource-Driven Framework for Configurable Entanglement in Quantum Networks
This work provides a foundational framework for configurable entanglement in quantum networks, enabling flexible connectivity without predetermined links, which is important for future quantum internet architectures.
The paper proposes a resource-driven framework for quantum networks where multipartite entanglement is treated as a programmable resource that can be reconfigured into different entanglement-connectivity graphs via LOCC. It introduces Entanglement Rolling as a measurement-based protocol and shows, using the Noisy Stabilizer Formalism, that the approach maintains reliable performance under realistic noise conditions.
Shared multipartite entanglement defines a ``whatever channel'', i.e., a latent communication substrate that does not determine a priori which end-to-end entangled links are activated, but can be configured to support different entanglement-connectivity graphs through Local Operations and Classical Communication (LOCC). Building on this, we propose a resource-driven framework in which multipartite entanglement is treated as a programmable resource that induces a space of admissible entanglement-graph configurations. Within this framework, connectivity provisioning emerges as a particular instance of a more general resource reconfiguration process. To support this paradigm, we introduce a set of structural design parameters that characterize the operational degrees of freedom of the resource and define the admissible transformations independently of the specific mechanism used to realize them. We then formalize Entanglement Rolling as a measurement-based protocol that operates over the induced configuration space, enabling the systematic reconfiguration of the shared resource across a family of multipartite states. Finally, we analyze the proposed framework under realistic noise conditions. Leveraging the Noisy Stabilizer Formalism (NSF), we derive closed-form noise maps that characterize the effect of noise on the resource transformations and show that the proposed approach maintains reliable performance under relevant noise processes.