Expand this Topic clickable element to expand a topic
Skip to content
Optica Publishing Group
  • Journal of Lightwave Technology
  • Vol. 37,
  • Issue 1,
  • pp. 95-102
  • (2019)

Effect of Polarization Dependent Loss on the Quality of Transmitted Polarization Entanglement

Open Access Open Access

Abstract

Quantum networking brings together several diverse research areas, such as fiber-optic communication, quantum optics, and quantum information, to achieve capabilities in security, secret sharing, and authentication which are unavailable classically. The development of practical fiber-based quantum networks requires an understanding of the reach, rates, and quality of the entanglement of distributed quantum states. Here, we present a theoretical model describing how the magnitude and orientation of polarization dependent loss (PDL), a common impairment in fiber-optic networks, affects the entanglement quality of distributed quantum states. Furthermore, we theoretically characterize how PDL in one fiber channel can be optimally applied in order to nonlocally compensate for the PDL present in another channel. We present experimental results that verify our theoretical model.

© 2018 IEEE

PDF Article
More Like This
Loss of polarization entanglement in a fiber-optic system with polarization mode dispersion in one optical path

Misha Brodsky, Elizabeth C. George, Cristian Antonelli, and Mark Shtaif
Opt. Lett. 36(1) 43-45 (2011)

Counterfactual entanglement distribution without transmitting any particles

Qi Guo, Liu-Yong Cheng, Li Chen, Hong-Fu Wang, and Shou Zhang
Opt. Express 22(8) 8970-8984 (2014)

High-efficient entanglement distillation from photon loss and decoherence

Tie-Jun Wang and Chuan Wang
Opt. Express 23(24) 31550-31563 (2015)

Cited By

Optica participates in Crossref's Cited-By Linking service. Citing articles from Optica Publishing Group journals and other participating publishers are listed here.


Select as filters


Select Topics Cancel
© Copyright 2024 | Optica Publishing Group. All rights reserved, including rights for text and data mining and training of artificial technologies or similar technologies.