Experimental analysis of adaptive optics correction methods on the beam carrying orbital angular momentum mode through oceanic turbulence

Zhan, Haichao; Wang, Le; Wang, Wennai; Zhao, Shengmei

doi:10.1016/j.ijleo.2021.166990

Cited by 23 publications

(6 citation statements)

References 32 publications

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“…3(e), The intensity pattern of the compensated vortex beam is captured by the CCD2. Mode purity, also called transmission probability [7], can be used to evaluate the purity of OAM mode, which can be expressed as:…”

Section: Performance Evaluationmentioning

confidence: 99%

“…UWOC has many applications in the rapidly growing human underwater activities, such as submarines, autonomous underwater vehicles and unmanned underwater vehicles [5,6]. In order to meet the increasing demand for data transmission in military, civilian and commercial, orbital angular momentum (OAM) is introduced into UWOC [7]. In 1992, Allen et.al.…”

Section: Introductionmentioning

confidence: 99%

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Diffractive deep neural network based adaptive optics scheme for vortex beam in oceanic turbulence

Zhan,

Wang,

Wang

et al. 2022

Preprint

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Vortex beam carrying orbital angular momentum (OAM) is disturbed by oceanic turbulence (OT) when propagating in underwater wireless optical communication (UWOC) system. Adaptive optics (AO) is used to compensate for distortion and improve the performance of the UWOC system. In this work, we propose a diffractive deep neural network (DDNN) based AO scheme to compensate for the distortion caused by OT, where the DDNN is trained to obtain the mapping between the distortion intensity distribution of the vortex beam and its corresponding phase screen representating OT. The intensity pattern of the distorted vortex beam obtained in the experiment is input to the DDNN model, and the predicted phase screen can be used to compensate the distortion in real time. The experiment results show that the proposed scheme can extract quickly the characteristics of the intensity pattern of the distorted vortex beam, and output accurately the predicted phase screen. The mode purity of the compensated vortex beam is significantly improved, even with a strong OT. Our scheme may provide a new avenue for AO techniques, and is expected to promote the communication quality of UWOC system.

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Section: Performance Evaluationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Diffractive deep neural network based adaptive optics scheme for vortex beam in oceanic turbulence

Zhan,

Wang,

Wang

et al. 2022

Preprint

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“…Machine learning (ML) methods have also recently proved valuable in the context of the reconstruction of the properties of structured light. In particular, supervised and unsupervised learning techniques were used to classify OAM states propagating through free-space [91][92][93] and through turbulent environments [94][95][96][97][98][99][100][101][102][103][104], as well as to classify and reconstruct VVB states [105,106].…”

Section: Introductionmentioning

confidence: 99%

Enhanced detection techniques of Orbital Angular Momentum states in the classical and quantum regimes

Suprano,

Zia,

Polino

et al. 2022

Preprint

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The Orbital Angular Momentum (OAM) of light has been at the center of several classical and quantum applications for imaging, information processing and communication. However, the complex structure inherent in OAM states makes their detection and classification nontrivial in many circumstances. Most of the current detection schemes are based on models of the OAM states built upon the use of Laguerre-Gauss modes. However, this may not in general be sufficient to capture full information on the generated states. In this paper, we go beyond the Laguerre-Gauss assumption, and employ Hypergeometric-Gaussian modes as the basis states of a refined model that can be used -in certain scenarios -to better tailor OAM detection techniques. We show that enhanced performances in OAM detection are obtained for holographic projection via spatial light modulators in combination with single-mode fibers, and for classification techniques based on a machine learning approach. Furthermore, a three-fold enhancement in the single-mode fiber coupling efficiency is obtained for the holographic technique, when using the Hypergeometric-Gaussian model with respect to the Laguerre-Gauss one. This improvement provides a significant boost in the overall efficiency of OAM-encoded single-photon detection systems. Given that most of the experimental works using OAM states are effectively based on the generation of Hypergeometric-Gauss modes, our findings thus represent a relevant addition to experimental toolboxes for OAM-based protocols in quantum communication, cryptography and simulation.

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“…Machine learning (ML) techniques have recently been shown as a valuable tool to overcome the many experimental and theoretical limitations related to reconstructing OAM states. In particular, neural networks have been used to recognize and classify structured light states such as superposition of OAM [58][59][60][61][62][63] and vector vortex beams [64,65], also considering the propagation in turbulent environments [66][67][68][69][70][71][72][73][74][75][76][77][78]. * fabio.sciarrino@uniroma1.it In this context, most of the efforts have been focused on detecting the probability of finding OAM states in a fixed basis, as opposed to being able to resolve coherence terms between different modes.…”

Section: Introductionmentioning

confidence: 99%

Regression of high dimensional angular momentum states of light

Zia¹,

Checchinato²,

Suprano³

et al. 2022

Preprint

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The Orbital Angular Momentum (OAM) of light is an infinite-dimensional degree of freedom of light with several applications in both classical and quantum optics. However, to fully take advantage of the potential of OAM states, reliable detection platforms to characterize generated states in experimental conditions are needed. Here, we present an approach to reconstruct input OAM states from measurements of the spatial intensity distributions they produce. To obviate issues arising from intrinsic symmetry of Laguerre-Gauss modes, we employ a pair of intensity profiles per state projecting it only on two distinct bases, showing how this allows to uniquely recover input states from the collected data. Our approach is based on a combined application of dimensionality reduction via principal component analysis, and linear regression, and thus has a low computational cost during both training and testing stages. We showcase our approach in a real photonic setup, generating up-to-four-dimensional OAM states through a quantum walk dynamics. The high performances and versatility of the demonstrated approach make it an ideal tool to characterize high-dimensional states in quantum information protocols.

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Experimental analysis of adaptive optics correction methods on the beam carrying orbital angular momentum mode through oceanic turbulence

Cited by 23 publications

References 32 publications

Diffractive deep neural network based adaptive optics scheme for vortex beam in oceanic turbulence

Diffractive deep neural network based adaptive optics scheme for vortex beam in oceanic turbulence

Enhanced detection techniques of Orbital Angular Momentum states in the classical and quantum regimes

Regression of high dimensional angular momentum states of light

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