🤖 AI Summary
This work addresses the challenge of dynamic end-to-end entanglement demands in quantum networks by proposing a resource-driven paradigm for multipartite entanglement configuration. Treating shared multipartite entanglement as a programmable resource, the framework enables on-demand reconstruction of entanglement graphs within a configuration space defined by structural parameters, using only local operations and classical communication (LOCC). The core innovation lies in the Entanglement Rolling protocol, which integrates a noisy stabilizer formalism (NSF) to model typical noise processes and yields a closed-form noise map. Theoretical analysis demonstrates that this approach achieves controllable and reliable entanglement reconfiguration under realistic noise conditions, thereby unifying connectivity provisioning with dynamic resource scheduling in quantum networks.
📝 Abstract
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.