AEDGE: Atomic Experiment for Dark Matter and Gravity Exploration in Space
Applying machine learning optimization methods to the production of a quantum gas
Abstract:
We apply three machine learning strategies to optimize the atomic cooling processes utilized in the production of a Bose–Einstein condensate (BEC). For the first time, we optimize both laser cooling and evaporative cooling mechanisms simultaneously. We present the results of an evolutionary optimization method (differential evolution), a method based on non-parametric inference (Gaussian process regression) and a gradient-based function approximator (artificial neural network). Online optimization is performed using no prior knowledge of the apparatus, and the learner succeeds in creating a BEC from completely randomized initial parameters. Optimizing these cooling processes results in a factor of four increase in BEC atom number compared to our manually-optimized parameters. This automated approach can maintain close-to-optimal performance in long-term operation. Furthermore, we show that machine learning techniques can be used to identify the main sources of instability within the apparatus.(py)LIon: A package for simulating trapped ion trajectories
Abstract:
The (py)LIon package is a set of tools to simulate the classical trajectories of ensembles of ions in electrodynamic traps. Molecular dynamics simulations are performed using LAMMPS, an efficient and feature-rich program. (py)LIon has been validated by comparison with the analytic theory describing ion trap dynamics. Notable features include GPU-accelerated force calculations, and treating collections of ions as rigid bodies to enable investigations of the rotational dynamics of large, mesoscopic charged particles.
Programme summary
Program Title: (py)LIon
Program Files doi: http://dx.doi.org/10.17632/ywwd9nnxjh.1
Licencing provisions: MIT
Programming language: Matlab, Python
Subprograms used: LAMMPS
Nature of problem: Simulating the dynamics of ions and mesoscopic charged particles confined in an electrodynamic trap using molecular dynamics methods
Solution method: Provide a tested, feature-rich API to configure molecular dynamics calculations in LAMMPS
Unusual features: (py)LIon can treat collections of ions as rigid bodies to simulate larger objects confined in electrodynamic traps. GPU acceleration is provided through the LAMMPS package.