Author: Aslam, A.
Paper Title Page
WEPAB411 Ion Coulomb Crystals in Storage Rings for Quantum Information Science 3667
 
  • K.A. Brown, G.J. Mahler, T. Roser, T.V. Shaftan, Z. Zhao
    BNL, Upton, New York, USA
  • A. Aslam, S. Biedron, T.B. Bolin, C. Gonzalez-Zacarias, S.I. Sosa Guitron
    UNM-ECE, Albuquerque, USA
  • R. Chen, T.G. Robertazzi
    Stony Brook University, Stony Brook, New York, USA
  • B. Huang
    SBU, Stony Brook, USA
  
  Funding: Work supported by Brookhaven Science Associates, LLC under Contract No. DE-AC02-98CH10886 with the U.S. Department of Energy.
We discuss the possible use of crystalline beams in storage rings for applications in quantum information science (QIS). Crystalline beams have been created in ion trap systems and proven to be useful as a computational basis for QIS applications. The same structures can be created in a storage ring, but the ions necessarily have a constant velocity and are rotating in a circular trap. The basic structures that are needed are ultracold crystalline beams, called ion Coulomb crystals (ICC’s). We will describe different applications of ICC’s for QIS, how QIS information is obtained and can be used for quantum computing, and some of the challenges that need to be resolved to realize practical QIS applications in storage rings. 		 
DOI • reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-WEPAB411  
About • paper received ※ 19 May 2021       paper accepted ※ 20 July 2021       issue date ※ 20 August 2021  
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THPAB349 Feed-Forward Neural Network Based Modelling of an Ultrafast Laser for Enhanced Control 4478
 
  • A. Aslam, M. Martínez-Ramón, S.D. Scott
    UNM-ECE, Albuquerque, USA
  • S. Biedron
    Argonne National Laboratory, Office of Naval Research Project, Argonne, Illinois, USA
  • S. Biedron
    Element Aero, Chicago, USA
  • S. Biedron
    UNM-ME, Albuquerque, New Mexico, USA
  • M. Burger, J. Murphy
    NERS-UM, Ann Arbor, Michigan, USA
  • K.M. Krushelnick, J. Nees, A.G.R. Thomas
    University of Michigan, Ann Arbor, Michigan, USA
  • Y. Ma
    IHEP, Beijing, People’s Republic of China
  • Y. Ma
    Michigan University, Ann Arbor, Michigan, USA
  
  Funding: Acknowledgements: This material is based upon work supported by the U.S. Department of Energy, Office of Science, Office of High Energy Physics, under award number DE-SC0019468.
The applications of machine learning in today’s world encompass all fields of life and physical sciences. In this paper, we implement a machine learning based algorithm in the context of laser physics and particle accelerators. Specifically, a neural network-based optimisation algorithm has been developed that offers enhanced control over an ultrafast femtosecond laser in comparison to the traditional Proportional Integral and derivative (PID) controls. This research opens a new potential of utilising machine learning and even deep learning techniques to improve the performance of several different lasers and accelerators systems. 		 
DOI • reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-THPAB349  
About • paper received ※ 20 May 2021       paper accepted ※ 02 July 2021       issue date ※ 17 August 2021  
Export • reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)