Experimental Validation of Spectral-Spatial Power Evolution Design Using Raman Amplifiers

• URL: http://arxiv.org/abs/2206.07658v1
• Date: Mon, 16 May 2022 13:55:50 GMT
• Title: Experimental Validation of Spectral-Spatial Power Evolution Design Using Raman Amplifiers
• Abstract summary: We experimentally validate a machine learning-enabled Raman amplification framework, capable of jointly shaping the signal power evolution in two domains: frequency and fiber distance. The proposed experiment addresses the amplification in the whole C-band, by optimizing four first-order counter-propagating Raman pumps.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We experimentally validate a machine learning-enabled Raman amplification framework, capable of jointly shaping the signal power evolution in two domains: frequency and fiber distance. The proposed experiment addresses the amplification in the whole C-band, by optimizing four first-order counter-propagating Raman pumps.

Related papers

• Phase-Selective Amplitude Amplification for Constrained Optimization [0.0]
  This work introduces a variant of amplitude amplification using stabilizer and blade qubits to improve boosting robustness across objective distributions.<n>Formal performance bounds and larger-scale validation are left for future research.
  arXiv  Detail & Related papers  (2026-07-19T23:31:57Z)
• D2PO: Optimizing Diffusion Samplers via Dynamic Preference [55.530862428116244]
  D2PO is a principled framework for optimizing diffusion sampling policies.<n>We introduce dynamic preferences, where the preferred samples used for alignment progressively improve.<n>Experiments demonstrate that D2PO aligns diffusion samplers with perceptual quality more faithfully.
  arXiv  Detail & Related papers  (2026-07-07T06:05:42Z)
• Experimental loopback boson sampling [0.0]
  We present an experimental demonstration of boson sampling enhanced by optical feedback lines.<n>This work advances photonic quantum computing by demonstrating a resource-efficient method to increase sampling complexity.
  arXiv  Detail & Related papers  (2025-11-12T14:47:43Z)
• SpectrumFM: A New Paradigm for Spectrum Cognition [65.65474629224558]
  We propose a spectrum foundation model, termed SpectrumFM, which provides a new paradigm for spectrum cognition.<n>An innovative spectrum encoder that exploits the convolutional neural networks is proposed to effectively capture both fine-grained local signal structures and high-level global dependencies in the spectrum data.<n>Two novel self-supervised learning tasks, namely masked reconstruction and next-slot signal prediction, are developed for pre-training SpectrumFM, enabling the model to learn rich and transferable representations.
  arXiv  Detail & Related papers  (2025-08-02T14:40:50Z)
• Point-Calibrated Spectral Neural Operators [54.13671100638092]
  We introduce Point-Calibrated Spectral Transform, which learns operator mappings by approximating functions with the point-level adaptive spectral basis. Point-Calibrated Spectral Neural Operators learn operator mappings by approximating functions with the point-level adaptive spectral basis.
  arXiv  Detail & Related papers  (2024-10-15T08:19:39Z)
• On-the-Fly Ab Initio Hagedorn Wavepacket Dynamics: Single Vibronic Level Fluorescence Spectra of Difluorocarbene [0.0]
  Hagedorn wavepackets have been used to compute single vibronic level (SVL) spectra efficiently in model harmonic potentials. We show that, whereas global harmonic models are inadequate for CF2, the spectra computed with the on-the-fly local harmonic Hagedorn wavepacket dynamics agree well with experimental data.
  arXiv  Detail & Related papers  (2024-09-03T13:04:44Z)
• A Hybrid Transformer-Mamba Network for Single Image Deraining [70.64069487982916]
  Existing deraining Transformers employ self-attention mechanisms with fixed-range windows or along channel dimensions. We introduce a novel dual-branch hybrid Transformer-Mamba network, denoted as TransMamba, aimed at effectively capturing long-range rain-related dependencies.
  arXiv  Detail & Related papers  (2024-08-31T10:03:19Z)
• Spectral Adapter: Fine-Tuning in Spectral Space [45.72323731094864]
  We study the enhancement of current PEFT methods by incorporating the spectral information of pretrained weight matrices into the fine-tuning procedure. We show through extensive experiments that the proposed fine-tuning model enables better parameter efficiency and tuning performance as well as benefits multi-adapter fusion.
  arXiv  Detail & Related papers  (2024-05-22T19:36:55Z)
• Ab initio simulation of single vibronic level fluorescence spectra of anthracene using Hagedorn wavepackets [0.0]
  Single vibronic level (SVL) fluorescence spectroscopy contributes to the understanding of molecular vibrational structures and relaxation processes. Here, we present a practical method for computing SVL fluorescence spectra of polyatomic molecules from arbitrary initial vibrational levels.
  arXiv  Detail & Related papers  (2024-03-01T17:39:42Z)
• SHARCS: Efficient Transformers through Routing with Dynamic Width Sub-networks [93.0460007020805]
  We introduce SHARCS for adaptive inference that takes into account the hardness of input samples. SHARCS can train a router on any transformer network, enabling the model to direct different samples to sub-networks with varying widths.
  arXiv  Detail & Related papers  (2023-10-18T17:35:15Z)
• Enhancing Dispersive Readout of Superconducting Qubits Through Dynamic Control of the Dispersive Shift: Experiment and Theory [47.00474212574662]
  A superconducting qubit is coupled to a large-bandwidth readout resonator. We show a beyond-state-of-the-art two-state-readout error of only 0.25,%$ in 100 ns integration time. The presented results are expected to further boost the performance of new and existing algorithms and protocols.
  arXiv  Detail & Related papers  (2023-07-15T10:30:10Z)
• Hyper-entanglement between pulse modes and frequency bins [101.18253437732933]
  Hyper-entanglement between two or more photonic degrees of freedom (DOF) can enhance and enable new quantum protocols. We demonstrate the generation of photon pairs hyper-entangled between pulse modes and frequency bins.
  arXiv  Detail & Related papers  (2023-04-24T15:43:08Z)
• Flexible Raman Amplifier Optimization Based on Machine Learning-aided Physical Stimulated Raman Scattering Model [1.0499611180329804]
  A differentiable function is obtained for the Raman gain gradient coefficient using machine learning (ML) Both the frequency and power of an arbitrary number of pumps in a forward pumping configuration are optimized for an arbitrary data channel load and span length. A gain flatness of $$ 1dB over 4 THz is achieved.
  arXiv  Detail & Related papers  (2022-06-13T09:08:18Z)
• Quantum-Enhanced Two-Photon Spectroscopy Using Two-mode Squeezed Light [0.0]
  We investigate the prospects of using two-mode intensity squeezed twin-beams, generated in Rb vapor, to improve the sensitivity of spectroscopic measurements. As a proof of principle demonstration, we demonstrated the quantum-enhanced measurements of the Rb $5D_3/2$ hyperfine structure.
  arXiv  Detail & Related papers  (2020-12-26T14:29:26Z)
• Exploring entanglement in open cavity parametric oscillators: from triply to doubly resonant cavities [0.0]
  We demonstrate the entanglement of the pump mode with converted fields in both situations. The strong correlations involving the sidebands of the pump and converted fields gives the signatures of a rich dynamic of multipartite entanglement.
  arXiv  Detail & Related papers  (2020-04-13T22:11:07Z)

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.