Quantum Compression for Distributed Entanglement

TL;DR

This paper develops compression strategies for distributing multipartite entanglement efficiently when the partitioning is uncertain, optimizing resource states and compression schemes to maximize entanglement under transmission constraints.

Contribution

It introduces a joint design framework for resource states and compression schemes, deriving bounds and practical constructions for entanglement distribution under uncertainty.

Findings

Derived non-asymptotic bounds on achievable entanglement and coding rates.

Proposed efficient optimization methods exploiting symmetry in weighted Dicke states.

Constructed practical schemes approaching theoretical bounds for bipartite and multipartite cases.

Abstract

We study compression strategies for multipartite entanglement distribution under uncertainty in the partitioning of the quantum state. When the partition is not known at the time of state preparation, we show that a joint design of the resource state and a family of compression schemes can increase the entanglement across partitions under a fixed transmission budget. We formulate this as a source coding problem and derive non-asymptotic upper and lower bounds on the achievable average entanglement subject to an average coding rate. We furthermore design an efficient method for jointly optimizing states and lossless compression maps by exploiting the inherent symmetry of weighted Dicke states. In the bipartite case, we propose practical constructions that closely approach the derived upper bound, and more generally we provide practical constructions for multipartite settings.