Generation and evaluation of multipartite entanglement with multi-rail encoding in linear optics networks

TL;DR

This paper develops a theoretical framework for detecting and generating multipartite entanglement in linear optics networks using multi-rail encoding, combining discrete and continuous-variable systems, and analyzes photon loss effects.

Contribution

It introduces a theory for detecting genuine multipartite entanglement in multi-rail encoded linear optics systems and proposes a scheme for entanglement generation combining discrete and continuous variables.

Findings

Established a detection theory for multipartite entanglement in linear optics networks.

Proposed a scheme for generating GME using both single photons and squeezed states.

Analyzed the impact of photon losses on the entanglement generation scheme.

Abstract

A linear optics network is a multimode interferometer system, where indistinguishable photon inputs can create nonclassical interference that can not be simulated with classical computers. Such nonclassical interference implies the existence of entanglement among its subsystems, if we divide its modes into different parties. Entanglement in such systems is naturally encoded in multi-rail (multi-mode) quantum registers. For bipartite entanglement, a generation and detection scheme with multi-rail encoding has been theoretically proposed [NJP 19(10):103032, 2017] and experimentally realized [Optica, 7(11):1517, 2020]. In this paper, we will take a step further to establish a theory for the detection of multi-rail-encoded discrete-variable genuine multipartite entanglement (GME) in fixed local-photon-number subspaces of linear optics networks. We also propose a scheme for GME generation with both discrete-variable (single photons) and continuous-variable (squeezed states) light sources. This scheme allows us to reveal the discrete-variable GME in continuous-variable systems. The effect of photon losses is also numerically analyzed for the generation scheme based on continuous-variable inputs.