Machine-learning recognition of light orbital-angular-momentum superpositions

Silva, B. Pinheiro da; Marques, Bruno Augusto Dorta; Rodrigues, Rafael B.; Ribeiro, P. H. Souto; Khoury, A. Z.

doi:10.1103/physreva.103.063704

Cited by 40 publications

(13 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…symmetrically along the optical axis (m = n = 0) where the correlation peak for pattern 'c' (l = 0) is located. The phase-singularities can be discerned from the phase distributions, which can be detected using spiral imaging [45], quantum-based vortex mapping [30,46] or via machine learning recognition [47].…”

Section: Encoding Oam Statesmentioning

confidence: 99%

Holographic photonic neuron

Daria

2021

Neuromorph. Comput. Eng.

View full text Add to dashboard Cite

The promise of artificial intelligence (AI) to process complex datasets has brought about innovative computing paradigms. While recent developments in quantum-photonic computing have reached significant feats, mimicking our brain’s ability to recognize images are poorly integrated in these ventures. Here, I incorporate orbital angular momentum (OAM) states in a classical Vander Lugt optical correlator to create the holographic photonic neuron (HoloPheuron). The HoloPheuron can memorize an array of matched filters in a single phase-hologram, which is derived by linking OAM states with elements in the array. Successful correlation is independent of intensity and yields photons with OAM states of lℏ, which can be used as a transmission protocol or qudits for quantum computing. The unique OAM identifier establishes the HoloPheuron as a fundamental AI device for pattern recognition that can be scaled and integrated with other computing platforms to build-up a neuromorphic quantum-photonic processor that mimics the brain

show abstract

Section: Encoding Oam Statesmentioning

confidence: 99%

Holographic photonic neuron

Daria

2021

Neuromorph. Comput. Eng.

View full text Add to dashboard Cite

show abstract

“…The deep learning model updates the weights via backpropagation and then extracts features from massive data without human intervention or prior knowledge of physics and the experimental system. Because of these advantages, physicists have constructed complex neural networks to complete numerous tasks, including far-field subwavelength acoustic imaging 18 , value estimation of a stochastic magnetic field 19 , vortex light recognition 20 , 21 , demultiplexing of an orbital angular momentum beam 22 , 23 and automatic control of experiments 24 – 29 .…”

Section: Introductionmentioning

confidence: 99%

Deep learning enhanced Rydberg multifrequency microwave recognition

et al. 2022

View full text Add to dashboard Cite

Recognition of multifrequency microwave (MW) electric fields is challenging because of the complex interference of multifrequency fields in practical applications. Rydberg atom-based measurements for multifrequency MW electric fields is promising in MW radar and MW communications. However, Rydberg atoms are sensitive not only to the MW signal but also to noise from atomic collisions and the environment, meaning that solution of the governing Lindblad master equation of light-atom interactions is complicated by the inclusion of noise and high-order terms. Here, we solve these problems by combining Rydberg atoms with deep learning model, demonstrating that this model uses the sensitivity of the Rydberg atoms while also reducing the impact of noise without solving the master equation. As a proof-of-principle demonstration, the deep learning enhanced Rydberg receiver allows direct decoding of the frequency-division multiplexed signal. This type of sensing technology is expected to benefit Rydberg-based MW fields sensing and communication.

show abstract

“…Apart from recognizing OAM modes in fractions other than integers, distinguishing between positive and negative topological charges is also of interest to the researchers. OAM modes that only differ in the signs of topological charges cannot be distinguished by a direct intensity measurement because they have identical intensity distributions [31]; therefore, an astigmatic transformation was utilized in [32] to generate new images that are distinguishable for those OAM modes. Every new transformed image was combined with its original direct image to form a so-called "two-channel" image that would be fed to a residual network.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Recognition of Orbital Angular Momentum Modes Over Atmospheric Turbulence Channels Assisted by Vortex Phase Modulation

Xiang

Zeng

et al. 2022

IEEE Photonics J.

View full text Add to dashboard Cite

Accurate recognition of orbital angular momentum (OAM) modes is a major challenge for OAM-based optical communications over atmospheric turbulence channels. The turbulence-induced distortions cause difficulties for the receiver to distinguish between adjacent OAM modes. Deep learning, such as convolutional neural networks (CNN), has been a promising technique in solving this problem. To improve the recognition performance, we propose a vortex modulation method that can magnify the subtle differences between closely adjacent OAM states. This allows the CNN to capture the image features more effectively and to recognize the topological charges more accurately. Numerical results show high recognition accuracy for both integer topological charges and fractional ones even under strong turbulence intensity and long propagation distance, which demonstrate the utility of the proposed method.

show abstract

Machine-learning recognition of light orbital-angular-momentum superpositions

Cited by 40 publications

References 36 publications

Holographic photonic neuron

Holographic photonic neuron

Deep learning enhanced Rydberg multifrequency microwave recognition

Deep Learning Recognition of Orbital Angular Momentum Modes Over Atmospheric Turbulence Channels Assisted by Vortex Phase Modulation

Contact Info

Product

Resources

About