Deterministic distribution of multipartite entanglement and steering in a quantum network by separable states
Meihong Wang, Yu Xiang, Haijun Kang, Dongmei Han, Yang Liu, Qiongyi, He, Qihuang Gong, Xiaolong Su, and Kunchi Peng

TL;DR
This paper demonstrates a method for deterministically distributing Gaussian entanglement and steering among multiple users in a quantum network using separable states, which is robust against loss and enables advanced quantum communication tasks.
Contribution
The authors experimentally show that entanglement and steering can be distributed among independent users via separable states, creating a new approach for quantum network resource sharing.
Findings
Distributed Gaussian entanglement is robust against channel loss.
One-way Gaussian steering among users is achieved.
Separable states can be used to generate entanglement during distribution.
Abstract
As two valuable quantum resources, Einstein-Podolsky-Rosen entanglement and steering play important roles in quantum-enhanced communication protocols. Distributing such quantum resources among multiple remote users in a network is a crucial precondition underlying various quantum tasks. We experimentally demonstrate the deterministic distribution of two- and three-mode Gaussian entanglement and steering by transmitting separable states in a network consisting of a quantum server and multiple users. In our experiment, entangled states are not prepared solely by the quantum server, but are created among independent users during the distribution process. More specifically, the quantum server prepares separable squeezed states and applies classical displacements on them before spreading out, and users simply perform local beam-splitter operations and homodyne measurements after they receive…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
