Feedback Intensity Equalization Algorithm for Multi-Spots Holographic Tweezer

• URL: http://arxiv.org/abs/2407.17049v2
• Date: Wed, 6 Nov 2024 01:05:04 GMT
• Title: Feedback Intensity Equalization Algorithm for Multi-Spots Holographic Tweezer
• Authors: Shaoxiong Wang, Yifei Hu, Yaoting Zhou, Peng Lan, Heng Shen, Zhongxiao Xu,
• Abstract summary: In holographic tweezer array experiment, optical tweezer generated by spatial light modulator (SLM) usually is used as static tweezer array. The uniformity of tweezer can exceed 96% when the number of tweezer size is bigger than 1000.
• Score: 2.1123021714870633
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Thanks to the high degree of adjustability, holographic tweezer array has been proved to be the best choice to create arbitrary geometries atomic array. In holographic tweezer array experiment, optical tweezer generated by spatial light modulator (SLM) usually is used as static tweezer array. Due to the alternating current(AC) stark shifts effect, intensity difference of traps will cause different light shift. So, the optimization of intensity equalization is very important in many-body system consist of single atoms. Here we report a work on studying of intensity equalization algorithm. Through this algorithm, the uniformity of tweezer can exceed 96% when the number of tweezer size is bigger than 1000. Our analysis shows that further uniformity requires further optimization of optical system. The realization of the intensity equalization algorithm is of great significance to the many-body experiments based on single atom array.

Related papers

• Accelerating Sinkhorn Algorithm with Sparse Newton Iterations [14.094908995798757]
  We present Sinkhorn-Newton-Sparse (SNS), an extension to the Sinkhorn algorithm. SNS converges orders magnitude faster across a wide range of practical cases.
  arXiv  Detail & Related papers  (2024-01-20T21:23:09Z)
• Optical tweezer generation using automated alignment and adaptive optics [0.0]
  State-of-the-art precision of optical alignment to achieve fine-tuning is reaching the limits of manual control. One of the elementary techniques of manual alignment of optics is cross-walking of laser beams. We apply this technique to mechanically align high numerical aperture objectives and show that we can produce high-quality tweezers.
  arXiv  Detail & Related papers  (2023-12-20T18:40:37Z)
• An Efficient Algorithm for Clustered Multi-Task Compressive Sensing [60.70532293880842]
  Clustered multi-task compressive sensing is a hierarchical model that solves multiple compressive sensing tasks. The existing inference algorithm for this model is computationally expensive and does not scale well in high dimensions. We propose a new algorithm that substantially accelerates model inference by avoiding the need to explicitly compute these covariance matrices.
  arXiv  Detail & Related papers  (2023-09-30T15:57:14Z)
• Single-photon sub-Rayleigh precision measurements of a pair of incoherent sources of unequal intensity [0.0]
  We consider single-photon imaging of two point-like emitters of unequal intensity. We employ multi-plane light conversion technology to experimentally implement Hermite-Gaussian spatial-mode demultiplexing.
  arXiv  Detail & Related papers  (2023-09-05T14:58:34Z)
• Intensity effects of light coupling to one- or two-atom arrays of infinite extent [0.0]
  We investigate coherent reflection and transmission and incoherent scattering of photons from a single array and from a pair of arrays. For the single array case, we show how increasing the light intensity changes the probabilities for these different processes. For a pair of parallel arrays at near resonant separation, the effects from increasing light intensity can become apparent with incredibly low intensity light.
  arXiv  Detail & Related papers  (2023-04-19T15:25:34Z)
• Optimal Algorithms for the Inhomogeneous Spiked Wigner Model [89.1371983413931]
  We derive an approximate message-passing algorithm (AMP) for the inhomogeneous problem. We identify in particular the existence of a statistical-to-computational gap where known algorithms require a signal-to-noise ratio bigger than the information-theoretic threshold to perform better than random.
  arXiv  Detail & Related papers  (2023-02-13T19:57:17Z)
• Efficient two-dimensional defect-free dual-species atom arrays rearrangement algorithm with near-fewest atom moves [12.346877792340315]
  We propose an efficient connectivity optimization algorithm (HCOA) to rearrange theally loaded atoms into arbitrary configurations. Our algorithm shows a high success rate (> 97%), low extra atom moves ratio, good scalability, and flexibility.
  arXiv  Detail & Related papers  (2022-03-22T17:03:09Z)
• Amortized Implicit Differentiation for Stochastic Bilevel Optimization [53.12363770169761]
  We study a class of algorithms for solving bilevel optimization problems in both deterministic and deterministic settings. We exploit a warm-start strategy to amortize the estimation of the exact gradient. By using this framework, our analysis shows these algorithms to match the computational complexity of methods that have access to an unbiased estimate of the gradient.
  arXiv  Detail & Related papers  (2021-11-29T15:10:09Z)
• Provably Faster Algorithms for Bilevel Optimization [54.83583213812667]
  Bilevel optimization has been widely applied in many important machine learning applications. We propose two new algorithms for bilevel optimization. We show that both algorithms achieve the complexity of $mathcalO(epsilon-1.5)$, which outperforms all existing algorithms by the order of magnitude.
  arXiv  Detail & Related papers  (2021-06-08T21:05:30Z)
• Regularization by Denoising Sub-sampled Newton Method for Spectral CT Multi-Material Decomposition [78.37855832568569]
  We propose to solve a model-based maximum-a-posterior problem to reconstruct multi-materials images with application to spectral CT. In particular, we propose to solve a regularized optimization problem based on a plug-in image-denoising function. We show numerical and experimental results for spectral CT materials decomposition.
  arXiv  Detail & Related papers  (2021-03-25T15:20:10Z)
• Single-Timescale Stochastic Nonconvex-Concave Optimization for Smooth Nonlinear TD Learning [145.54544979467872]
  We propose two single-timescale single-loop algorithms that require only one data point each step. Our results are expressed in a form of simultaneous primal and dual side convergence.
  arXiv  Detail & Related papers  (2020-08-23T20:36:49Z)

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.