Efficient sorting of orbital-angular-momentum states with large topological charges and their unknown superpositions via machine learning

• URL: http://arxiv.org/abs/2105.13621v1
• Date: Fri, 28 May 2021 06:56:46 GMT
• Title: Efficient sorting of orbital-angular-momentum states with large topological charges and their unknown superpositions via machine learning
• Authors: Ling-Feng Zhang, Ya-Yi Lin, Zhen-Yue She, Zhi-Hao Huang, Jia-Zhen Li, Hui Yan, Wei Huang, Dan-Wei Zhang, and Shi-Liang Zhu
• Abstract summary: Light beams carrying orbital-angular-momentum (OAM) play an important role in optical manipulation and communication owing to their unbounded state space. Here we demonstrate that neural networks can be trained to sort OAM modes with large topological charges and unknown superpositions.
• Score: 3.4467006377108143
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Light beams carrying orbital-angular-momentum (OAM) play an important role in optical manipulation and communication owing to their unbounded state space. However, it is still challenging to efficiently discriminate OAM modes with large topological charges and thus only a small part of the OAM states have been usually used. Here we demonstrate that neural networks can be trained to sort OAM modes with large topological charges and unknown superpositions. Using intensity images of OAM modes generalized in simulations and experiments as the input data, we illustrate that our neural network has great generalization power to recognize OAM modes of large topological charges beyond training areas with high accuracy. Moreover, the trained neural network can correctly classify and predict arbitrary superpositions of two OAM modes with random topological charges. Our machine learning approach only requires a small portion of experimental samples and significantly reduces the cost in experiments, which paves the way to study the OAM physics and increase the state space of OAM beams in practical applications.

Related papers

• Deep-learning-assisted optical communication with discretized state space of structured light [2.884252230064288]
  We present a novel method that uses the advanced deep learning technique for LG modes recognition. A proof-of-principle experiment is also performed, showing that our method effectively categorizes OAM states with small training samples and high accuracy. This work opens up a new avenue for achieving high-capacity optical communication with low OAM number based on structured light.
  arXiv  Detail & Related papers  (2024-03-14T15:03:09Z)
• Closely-Spaced Object Classification Using MuyGPyS [0.6144680854063939]
  We present a novel method for detecting closely-spaced objects (CSO) in optical space domain awareness (SDA) algorithms. We use the Gaussian process python package, MuyGPyS, and examine classification accuracy as a function of angular separation and magnitude difference between simulated satellites. We find that MuyGPyS outperforms traditional machine learning methods, especially under more challenging circumstances.
  arXiv  Detail & Related papers  (2023-11-17T22:52:46Z)
• Efficient Classical Shadow Tomography through Many-body Localization Dynamics [4.923287660970805]
  We introduce an alternative approach founded on the dynamics of many-body localization. We demonstrate that our scheme achieves remarkable efficiency comparable to shallow circuits.
  arXiv  Detail & Related papers  (2023-09-03T19:50:28Z)
• Data-Efficient Learning via Minimizing Hyperspherical Energy [48.47217827782576]
  This paper considers the problem of data-efficient learning from scratch using a small amount of representative data. We propose a MHE-based active learning (MHEAL) algorithm, and provide comprehensive theoretical guarantees for MHEAL.
  arXiv  Detail & Related papers  (2022-06-30T11:39:12Z)
• Regression of high dimensional angular momentum states of light [47.187609203210705]
  We present an approach to reconstruct input OAM states from measurements of the spatial intensity distributions they produce. We showcase our approach in a real photonic setup, generating up-to-four-dimensional OAM states through a quantum walk dynamics.
  arXiv  Detail & Related papers  (2022-06-20T16:16:48Z)
• Enhanced detection techniques of Orbital Angular Momentum states in the classical and quantum regimes [48.7576911714538]
  Complex structure inherent in Orbital Angular Momentum (OAM) states makes their detection and classification nontrivial. Most of the current detection schemes are based on models of the OAM states built upon the use of Laguerre-Gauss modes. We 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.
  arXiv  Detail & Related papers  (2022-01-19T18:46:34Z)
• Provable RL with Exogenous Distractors via Multistep Inverse Dynamics [85.52408288789164]
  Real-world applications of reinforcement learning (RL) require the agent to deal with high-dimensional observations such as those generated from a megapixel camera. Prior work has addressed such problems with representation learning, through which the agent can provably extract endogenous, latent state information from raw observations. However, such approaches can fail in the presence of temporally correlated noise in the observations.
  arXiv  Detail & Related papers  (2021-10-17T15:21:27Z)
• QuTiP-BoFiN: A bosonic and fermionic numerical hierarchical-equations-of-motion library with applications in light-harvesting, quantum control, and single-molecule electronics [51.15339237964982]
  "hierarchical equations of motion" (HEOM) is a powerful exact numerical approach to solve the dynamics. It has been extended and applied to problems in solid-state physics, optics, single-molecule electronics, and biological physics. We present a numerical library in Python, integrated with the powerful QuTiP platform, which implements the HEOM for both bosonic and fermionic environments.
  arXiv  Detail & Related papers  (2020-10-21T07:54:56Z)
• A Trainable Optimal Transport Embedding for Feature Aggregation and its Relationship to Attention [96.77554122595578]
  We introduce a parametrized representation of fixed size, which embeds and then aggregates elements from a given input set according to the optimal transport plan between the set and a trainable reference. Our approach scales to large datasets and allows end-to-end training of the reference, while also providing a simple unsupervised learning mechanism with small computational cost.
  arXiv  Detail & Related papers  (2020-06-22T08:35:58Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.