Combating errors in propagation of orbital angular momentum modes of light in turbulent media

• URL: http://arxiv.org/abs/2208.05156v2
• Date: Thu, 27 Oct 2022 11:08:42 GMT
• Title: Combating errors in propagation of orbital angular momentum modes of light in turbulent media
• Authors: Rajni Bala, Sooryansh Asthana, V. Ravishankar
• Abstract summary: We identify invariants for propagation of OAM modes through atmospheric and oceanic turbulence. We then develop a method for combating errors in what we call an idealised crosstalk channel. We construct quantum error correction and rejection codes for idealised crosstalk channels.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: There is a wealth of simulation, experimental and analytical studies on propagation of orbital angular momentum (OAM) modes through atmospheric and oceanic turbulence.} Using the data of these studies and generalising the framework proposed in [Bala et al., [arXiv:2208.04555] for error-immune information transfer, we accomplish two tasks. First, we identify invariants for propagation of OAM modes through atmospheric and oceanic turbulence, in which error-immune information can be encoded. A closer look at the data reveals two universal features: (i)coherence lasts for a much longer distance in turbulence than entanglement, and, (ii) the crosstalk among different OAM modes depends very weakly on the initial OAM mode index in the weak turbulence regime. Keeping these in mind, we next develop a method for combating errors in what we call an idealised crosstalk channel. In an idealised crosstalk channel, the crossover probabilities are independent of the initial mode index (IMI). We lay down a procedure that allows to retrieve full information in a state by identifying invariant quantities. Finally, we construct quantum error correction and rejection codes for idealised crosstalk channels, without any need for multiparty entanglement.

Related papers

• Straightness of Rectified Flow: A Theoretical Insight into Wasserstein Convergence [54.580605276017096]
  Rectified Flow (RF) aims to learn straight flow trajectories from noise to data using a sequence of convex optimization problems. RF theoretically straightens the trajectory through successive rectifications, reducing the number of evaluations function (NFEs) while sampling. We provide the first theoretical analysis of the Wasserstein distance between the sampling distribution of RF and the target distribution.
  arXiv  Detail & Related papers  (2024-10-19T02:36:11Z)
• Superresolution in separation estimation between two dynamic incoherent sources using spatial demultiplexing [0.0]
  Recently, perfect measurement based on spatial mode demultiplexing (SPADE) in Hermite-Gauss modes allowed one to reach the quantum limit of precision for estimation of separation between two weak incoherent stationary sources. In this paper, we consider another deviation from the perfect setup by discarding the assumption about the stationarity of the sources. We formulate a measurement algorithm that allows for the reduction of one parameter for estimation in the stationary sources scenario.
  arXiv  Detail & Related papers  (2024-07-15T07:57:57Z)
• Switched Flow Matching: Eliminating Singularities via Switching ODEs [12.273757042838675]
  Continuous-time generative models, such as Flow Matching (FM), construct probability paths to transport between one distribution and another. During inference, however, the learned model often requires multiple neural network evaluations to accurately integrate the flow. We propose Switched FM (SFM), that eliminates singularities via switching ODEs, as opposed to using a uniform ODE in FM.
  arXiv  Detail & Related papers  (2024-05-19T16:21:04Z)
• Deep Data Consistency: a Fast and Robust Diffusion Model-based Solver for Inverse Problems [0.0]
  We propose Deep Data Consistency (DDC) to update the data consistency step with a deep learning model when solving inverse problems with diffusion models. In comparison with state-of-the-art methods in linear and non-linear tasks, DDC demonstrates its outstanding performance of both similarity and realness metrics.
  arXiv  Detail & Related papers  (2024-05-17T12:54:43Z)
• Theory of Multimode Squeezed Light Generation in Lossy Media [0.0]
  A unified theoretical approach to describe the properties of multimode squeezed light generated in a lossy medium is presented. For an important class of Gaussian states, we derive master equations for the second-order correlation functions. Various techniques and strategies to introduce broadband modes can be considered.
  arXiv  Detail & Related papers  (2024-03-08T12:30:34Z)
• A Geometric Perspective on Diffusion Models [57.27857591493788]
  We inspect the ODE-based sampling of a popular variance-exploding SDE. We establish a theoretical relationship between the optimal ODE-based sampling and the classic mean-shift (mode-seeking) algorithm.
  arXiv  Detail & Related papers  (2023-05-31T15:33:16Z)
• A Variational Perspective on Solving Inverse Problems with Diffusion Models [101.831766524264]
  Inverse tasks can be formulated as inferring a posterior distribution over data. This is however challenging in diffusion models since the nonlinear and iterative nature of the diffusion process renders the posterior intractable. We propose a variational approach that by design seeks to approximate the true posterior distribution.
  arXiv  Detail & Related papers  (2023-05-07T23:00:47Z)
• Reflected Diffusion Models [93.26107023470979]
  We present Reflected Diffusion Models, which reverse a reflected differential equation evolving on the support of the data. Our approach learns the score function through a generalized score matching loss and extends key components of standard diffusion models.
  arXiv  Detail & Related papers  (2023-04-10T17:54:38Z)
• Diffusion Posterior Sampling for General Noisy Inverse Problems [50.873313752797124]
  We extend diffusion solvers to handle noisy (non)linear inverse problems via approximation of the posterior sampling. Our method demonstrates that diffusion models can incorporate various measurement noise statistics.
  arXiv  Detail & Related papers  (2022-09-29T11:12:27Z)
• Leveraging Global Parameters for Flow-based Neural Posterior Estimation [90.21090932619695]
  Inferring the parameters of a model based on experimental observations is central to the scientific method. A particularly challenging setting is when the model is strongly indeterminate, i.e., when distinct sets of parameters yield identical observations. We present a method for cracking such indeterminacy by exploiting additional information conveyed by an auxiliary set of observations sharing global parameters.
  arXiv  Detail & Related papers  (2021-02-12T12:23:13Z)
• Towards Out-of-Distribution Detection with Divergence Guarantee in Deep Generative Models [22.697643259435115]
  Deep generative models may assign higher likelihood to out-of-distribution (OOD) data than in-distribution (ID) data. We prove theorems to investigate the divergences in flow-based model. We propose two group anomaly detection methods.
  arXiv  Detail & Related papers  (2020-02-09T09:54:12Z)

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.