Anisotropic non-Kolmogorov turbulence phase screens with variable orientation

Bos, Jeremy P.; Roggemann, Michael C.; Gudimetla, V. S. Rao

doi:10.1364/ao.54.002039

Cited by 48 publications

(17 citation statements)

References 28 publications

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“…[ 67 ] We use the Kolmogorov model of turbulence to simulate the turbulent communication channel. [ 30,59,68 ] Turbulence induces a random modulation of the index of refraction that results from inhomogeneities of temperature and pressure of media. This, in turn, leads to distortions of the phase front of the spatial profile of optical modes.…”

Section: Experimental and Computational Methodsmentioning

confidence: 99%

Spatial Mode Correction of Single Photons Using Machine Learning

et al. 2021

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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, the self‐learning and self‐evolving features of artificial neural networks are exploited to correct the complex spatial profile of distorted Laguerre–Gaussian modes at the single‐photon level. Furthermore, the potential of this technique is used to improve the channel capacity of an optical communication protocol that relies on structured single photons. The results have important implications for real‐time turbulence correction of structured photons and single‐photon images.

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Section: Experimental and Computational Methodsmentioning

confidence: 99%

Spatial Mode Correction of Single Photons Using Machine Learning

et al. 2021

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“…This is quantified through the Fried's parameter r 0 , which is defined in terms of the standard refractive index C 2 n . In our experiment, the turbulence is simulated with a SLM by encoding random matrices with Kolmogrov statistics [26,43,46],…”

Section: Experimental and Computational Methodsmentioning

confidence: 99%

Spatial Mode Correction of Single Photons using Machine Learning

Bhusal,

Lohani,

You

et al. 2020

Preprint

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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 upon propagation, 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. Specifically, we demonstrate the possibility of correcting spatial modes distorted by thick atmospheric turbulence. Our results have important implications for real-time turbulence correction of structured photons and single-photon images.

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“…An interesting alternative to purely digital simulations are the laboratory benchtop systems, using real light as a source and a spatial light modulator (SLM) or a digital mirror device (DMD) as a simulator of a thin phase screen properly tuned to mimic an extended turbulence path [79]. The non-classic (anisotropic) turbulent thin phase screens were developed and studied in depth, e.g., in [80]. Unlike in computer simulations, having the possibility of using a large set of screens distributed along the propagation path, the SLM/DMD based simulations are limited to one, or possibly two to three screens because of the substantial power loss and the need for synchronization.…”

Section: Slm/dmd Benchtop Simulationsmentioning

confidence: 99%

Non-Classic Atmospheric Optical Turbulence: Review

Korotkova

Toselli

2021

Applied Sciences

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Theoretical models and results of experimental campaigns relating to non-classic regimes occurring in atmospheric optical turbulence are overviewed. Non-classic turbulence may manifest itself through such phenomena as a varying power law of the refractive-index power spectrum, anisotropy, the presence of constant-temperature gradients and coherent structures. A brief historical introduction to the theories of optical turbulence, both classic and non-classic, is first presented. The effects of non-classic atmospheric turbulence on propagating light beams are then discussed, followed by the summary of results on measuring the non-classic turbulence, on its computer and in-lab simulations and its controlled synthesis. The general theory based on the extended Huygens–Fresnel method, capable of quantifying various effects of non-classic turbulence on propagating optical fields, including the increased light diffraction, beam profile deformations, etc., is then outlined. The review concludes by a summary of optical engineering applications that can be influenced by atmospheric non-classic turbulence, e.g., remote sensing, imaging and wireless optical communication systems. The review makes an accent on the results developed by the authors for the recent AFOSR MURI project on deep turbulence.

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Anisotropic non-Kolmogorov turbulence phase screens with variable orientation

Cited by 48 publications

References 28 publications

Spatial Mode Correction of Single Photons Using Machine Learning

Spatial Mode Correction of Single Photons Using Machine Learning

Spatial Mode Correction of Single Photons using Machine Learning

Non-Classic Atmospheric Optical Turbulence: Review

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