Multiparameter optimisation of a magneto-optical trap using deep learning.

Journal: Nature communications
Published Date: Oct 19, 2018

Abstract

Machine learning based on artificial neural networks has emerged as an efficient means to develop empirical models of complex systems. Cold atomic ensembles have become commonplace in laboratories around the world, however, many-body interactions give rise to complex dynamics that preclude precise analytic optimisation of the cooling and trapping process. Here, we implement a deep artificial neural network to optimise the magneto-optic cooling and trapping of neutral atomic ensembles. The solution identified by machine learning is radically different to the smoothly varying adiabatic solutions currently used. Despite this, the solutions outperform best known solutions producing higher optical densities.

Authors

  • A D Tranter
    Centre for Quantum Computation and Communication Technologies, Department of Quantum Science, Research School of Physics and Engineering, The Australian National University, Acton, 2601, Australia.
  • H J Slatyer
    Centre for Quantum Computation and Communication Technologies, Department of Quantum Science, Research School of Physics and Engineering, The Australian National University, Acton, 2601, Australia.
  • M R Hush
    School of Engineering and Information Technology, University of New South Wales, Canberra, 2600, Australia.
  • A C Leung
    Centre for Quantum Computation and Communication Technologies, Department of Quantum Science, Research School of Physics and Engineering, The Australian National University, Acton, 2601, Australia.
  • J L Everett
    Centre for Quantum Computation and Communication Technologies, Department of Quantum Science, Research School of Physics and Engineering, The Australian National University, Acton, 2601, Australia.
  • K V Paul
    Centre for Quantum Computation and Communication Technologies, Department of Quantum Science, Research School of Physics and Engineering, The Australian National University, Acton, 2601, Australia.
  • P Vernaz-Gris
    Centre for Quantum Computation and Communication Technologies, Department of Quantum Science, Research School of Physics and Engineering, The Australian National University, Acton, 2601, Australia.
  • P K Lam
    Centre for Quantum Computation and Communication Technologies, Department of Quantum Science, Research School of Physics and Engineering, The Australian National University, Acton, 2601, Australia.
  • B C Buchler
    Centre for Quantum Computation and Communication Technologies, Department of Quantum Science, Research School of Physics and Engineering, The Australian National University, Acton, 2601, Australia. ben.buchler@anu.edu.au.
  • G T Campbell
    Centre for Quantum Computation and Communication Technologies, Department of Quantum Science, Research School of Physics and Engineering, The Australian National University, Acton, 2601, Australia.

Keywords

External Resources

Popular Topics
Recent Journals