Spatial Mode Correction of Single Photons using Machine Learning
- URL: http://arxiv.org/abs/2006.07760v2
- Date: Fri, 4 Sep 2020 14:53:55 GMT
- Title: Spatial Mode Correction of Single Photons using Machine Learning
- Authors: Narayan Bhusal, Sanjaya Lohani, Chenglong You, Mingyuan Hong, Joshua
Fabre, Pengcheng Zhao, Erin M. Knutson, Ryan T. Glasser, Omar S.
Magana-Loaiza
- Abstract summary: We exploit the self-learning and self-evolving features of artificial neural networks to correct the complex spatial profile of distorted Laguerre-Gaussian modes at the single-photon level.
Our results have important implications for real-time turbulence correction of structured photons and single-photon images.
- Score: 1.8086378019947618
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Spatial modes of light constitute valuable resources for a variety of quantum
technologies ranging from quantum communication and quantum imaging to remote
sensing. Nevertheless, their vulnerabilities to phase distortions, induced by
random media, impose significant limitations on the realistic implementation of
numerous quantum-photonic technologies. Unfortunately, this problem is
exacerbated at the single-photon level. Over the last two decades, this
challenging problem has been tackled through conventional schemes that utilize
optical nonlinearities, quantum correlations, and adaptive optics. In this
article, we exploit the self-learning and self-evolving features of artificial
neural networks to correct the complex spatial profile of distorted
Laguerre-Gaussian modes at the single-photon level. Furthermore, we demonstrate
the possibility of boosting the performance of an optical communication
protocol through the spatial mode correction of single photons using machine
learning. Our results have important implications for real-time turbulence
correction of structured photons and single-photon images.
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