Expand this Topic clickable element to expand a topic
Skip to content
Optica Publishing Group
  • Chinese Optics Letters
  • Vol. 14,
  • Issue 8,
  • pp. 080201-
  • (2016)

Interfering single photons retreived from collective atomic excitations in two dense cold-atom clouds

Not Accessible

Your library or personal account may give you access

Abstract

We report the Hong–Ou–Mandel (HOM) interference, with visibility of 91%, produced from two independent single photons retrieved from collective atomic excitations in two separate cold-atom clouds with high optical depths of 90. The high visibility of the HOM dip is ascribed to the pure single photon in the Fock state that was generated from a dense-cold-atom cloud pumping by a short pulse. The visibility is always the same regardless of the time response of the single-photon detectors. This result experimentally shows that the single photons retrieved are in a separable temporal state with their idler photons.

© 2016 Chinese Laser Press

PDF Article
More Like This
Quantum interference between autonomous single-photon sources from Doppler-broadened atomic ensembles

Taek Jeong, Yoon-Seok Lee, Jiho Park, Heonoh Kim, and Han Seb Moon
Optica 4(10) 1167-1170 (2017)

Two-photon excitation of launched cold atoms in flight

Rene Gonzalez, Eduardo Alejandro, Emma Erwin, and Anne L. Goodsell
J. Opt. Soc. Am. B 34(6) 1090-1096 (2017)

Measuring the frequency-time two-photon wavefunction of narrowband entangled photons from cold atoms via stimulated emission

Kwang-Kyoon Park, Jin-Hun Kim, Tian-Ming Zhao, Young-Wook Cho, and Yoon-Ho Kim
Optica 4(10) 1293-1297 (2017)

Cited By

You do not have subscription access to this journal. Cited by links are available to subscribers only. You may subscribe either as an Optica member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access Optica Member Subscription

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.