Ultracold quantum matter

Abstract:

© 2020 Elsevier B.V. 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 [Formula presented] package.

Abstract:

Quantum memories are essential for large-scale quantum information networks. Along with high efficiency, storage lifetime, and optical bandwidth, it is critical that the memory adds negligible noise to the recalled signal. A common source of noise in optical quantum memories is spontaneous four-wave mixing. We develop and implement a technically simple scheme to suppress this noise mechanism by means of quantum interference. Using this scheme with a Raman memory in warm atomic vapor, we demonstrate over an order of magnitude improvement in noise performance. Furthermore we demonstrate a method to quantify the remaining noise contributions and present a route to enable further noise suppression. Our scheme opens the way to quantum demonstrations using a broadband memory, significantly advancing the search for scalable quantum photonic networks.