High-sensitivity quantum sensing with pump-enhanced spontaneous
parametric down-conversion
- URL: http://arxiv.org/abs/2208.07595v1
- Date: Tue, 16 Aug 2022 08:14:27 GMT
- Title: High-sensitivity quantum sensing with pump-enhanced spontaneous
parametric down-conversion
- Authors: Chiara Lindner, Jachin Kunz, Simon J. Herr, Jens Kiessling, Sebastian
Wolf, Frank K\"uhnemann
- Abstract summary: We develop a quantum sensing concept based on correlated photon pairs generated by spontaneous parametric down-conversion (SPDC)
Using SPDC far from frequency degeneracy allows a 'division of labor' between the mid-infrared photon for strongest sample interaction and the correlated near-infrared photon for low-noise detection.
We demonstrate a nonlinear interferometer based on pump-enhanced SPDC with strongly improved emission rates, but maintaining broadband, spontaneous emission.
- Score: 1.3968276272277327
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: Recent years have seen the development of quantum sensing concepts utilizing
nonlinear interferometers based on correlated photon pairs generated by
spontaneous parametric down-conversion (SPDC). Using SPDC far from frequency
degeneracy allows a 'division of labor' between the mid-infrared photon for
strongest sample interaction and the correlated near-infrared photon for
low-noise detection. The small number of photons provided by SPDC and the
resulting inferior signal-to-noise ratio are, however, a limiting factor
preventing wide applicability of the novel sensing concept. Here, we
demonstrate a nonlinear interferometer based on pump-enhanced SPDC with
strongly improved emission rates, but maintaining broadband, spontaneous
emission. For validation of the concept, we demonstrate high-resolution
mid-infrared spectroscopy with near-infrared detection, showcasing the improved
accuracy. Although the number of mid-infrared photons is about five orders of
magnitude smaller than in classical spectrometers, the sensitivity of the
quantum spectrometer becomes comparable, marking an essential step toward
real-world applications.
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