Spatial Mode Correction of Single Photons Using Machine Learning

Bhusal, Narayan; Lohani, Sanjaya; You, Chenglong; Hong, Mingyuan; Fabre, Joshua; Pei, Zhao; Knutson, Erin M.; Glasser, Ryan T.; Magaña‐Loaiza, Omar S.

doi:10.1002/qute.202000103

Cited by 27 publications

(15 citation statements)

References 70 publications

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“…Machine learning and neural-networks-based algorithms have been recently used, among other things, to classify structured light [91,105,123] and for noise compensation [94][95][96][97][98][99][100][101][102]124]. CNNs are a class of neural-network architectures especially suited to process images for classification and regression tasks.…”

Section: Machine-learning-based Classificationmentioning

confidence: 99%

“…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%

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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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Section: Machine-learning-based Classificationmentioning

confidence: 99%

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

View full text Add to dashboard Cite

show abstract

“…The structure of the network consists of a group of interconnected neurons arranged in layers. Here, the information flows only in one direction, from input to output [37,38]. As indicated in Fig.…”

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confidence: 99%

Smart Quantum Statistical Imaging beyond the Abbe-Rayleigh Criterion

Bhusal¹,

Hong²,

Miller³

et al. 2021

Preprint

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The manifestation of the wave nature of light through diffraction imposes limits on the resolution of optical imaging. For over a century, the Abbe-Rayleigh criterion has been utilized to assess the spatial resolution limits of optical instruments. Recently, there has been an enormous impetus in overcoming the Abbe-Rayleigh resolution limit by projecting target light beams onto spatial modes. These conventional schemes for superresolution rely on a series of spatial projective measurements to pick up phase information that is used to boost the spatial resolution of optical systems. Unfortunately, these schemes require a priori information regarding the coherence properties of "unknown" light beams. Furthermore, they require stringent alignment and centering conditions that cannot be achieved in realistic scenarios. Here, we introduce a smart quantum camera for superresolving imaging. This camera exploits the self-learning features of artificial intelligence to identify the statistical fluctuations of unknown mixtures of light sources at each pixel. This is achieved through a universal quantum model that enables the design of artificial neural networks for the identification of quantum photon fluctuations. Our camera overcomes the inherent limitations of existing superresolution schemes based on spatial mode projection. Thus, our work provides a new perspective in the field of imaging with important implications for microscopy, remote sensing, and astronomy.

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“…Additionally, it has been shown that high-dimensional Hilbert spaces defined in the OAM basis can increase the robustness of secure protocols for quantum communication [13][14][15]. However, despite the enormous potential of structured photons, their vulnerabilities to phase distortions impose important limitations on the realistic implementation of quantum technologies [3,5,6,13,[16][17][18][19][20]. Indeed, LG beams are not eigenmodes of commercial optical fibers and consequently their spatial profile is not preserved upon propagation.…”

mentioning

confidence: 99%

“…In the field of photonics, there has been extensive research devoted to developing artificial neural networks for the implementation of novel optical instruments [33][34][35]. Indeed, convolutional neural networks (CNNs) have enabled the demonstration of new imaging schemes working at the single-photon level [17,36,37]. These protocols have been employed to characterize structured photons in the Laguerre-Gaussian (LG), Hermite-Gaussian (HG), and Bessel-Gaussian (BG) bases [3,5,6,[36][37][38][39][40][41].…”

mentioning

confidence: 99%

High-dimensional encryption in optical fibers using machine learning

Lollie¹,

Mostafavi²,

Bhusal³

et al. 2021

Preprint

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The ability to engineer the spatial wavefunction of photons has enabled a variety of quantum protocols for communication, sensing, and information processing. These protocols exploit the high dimensionality of structured light enabling the encodinng of multiple bits of information in a single photon, the measurement of small physical parameters, and the achievement of unprecedented levels of security in schemes for cryptography. Unfortunately, the potential of structured light has been restrained to free-space platforms in which the spatial profile of photons is preserved. Here, we make an important step forward to using structured light for fiber optical communication. We introduce a smart high-dimensional encryption protocol in which the propagation of spatial modes in multimode fibers is used as a natural mechanism for encryption. This provides a secure communication channel for data transmission. The information encoded in spatial modes is retrieved using artificial neural networks, which are trained from the intensity distributions of experimentally detected spatial modes. Our on-fiber communication platform allows us to use spatial modes of light for high-dimensional bit-by-bit and byte-by-byte encoding. This protocol enables one to recover messages and images with almost perfect accuracy. Our smart protocol for high-dimensional optical encryption in optical fibers has key implications for quantum technologies relying on structured fields of light, particularly those that are challenged by free-space propagation.

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Spatial Mode Correction of Single Photons Using Machine Learning

Cited by 27 publications

References 70 publications

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

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

Smart Quantum Statistical Imaging beyond the Abbe-Rayleigh Criterion

High-dimensional encryption in optical fibers using machine learning

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