Deep Learning Cold Atomic Ensembles for Quantum Memories

Aaron Tranter
March 15th, 2018 AARON TRANTER Australian National University in Canberra

Quantum memories are integral to the realization of quantum information networks and quantum information processing. A promising platform is gradient echo memory (GEM) in cold atomic systems with demonstrated efficiencies of ~=87%. We demonstrate the first application of a deep learning algorithm to a cold atomic system in order to increase the optical depth (OD) of our atomic trap and thus increase memory efficiency. We demonstrate an improvement in OD from 530 +- 8 to 970 +- 20 by performing an optimization over 63 experimental parameters. We also observe a physical change in the atomic cloud corresponding to the spatial distribution of the atom cloud and apply the optimization to the GEM protocol.

Seminar, March 15, 2018, 16:00. ICFO’s Blue Lecture Room

Hosted by Prof. Hugues de Riedmatten and Prof. Morgan Mitchell