Enhancing quantum simulators with neural networks
Last updated February 7, 2020 by Alessandro Ferraro
Wednesday, December 18th 2019, 02:00 PM, MAPTC/0G/017
Speaker: G. Torlai (Flatiron Institute)
The recent advances in qubit manufacturing and coherent control of synthetic quantum matter are leading to a new generation of intermediate-scale quantum hardware, with promising progress towards simulation of quantum matter and materials. In order to enhance the capabilities of this class of quantum devices, some of the more arduous experimental tasks can be off-loaded to data-driven classical algorithms running on conventional computers. I will review recent efforts in deploying machine learning on data generated by cold-atom quantum simulators and superconducting quantum circuits.
We are a Research Cluster of the School of Mathematics and Physics at Queen’s University Belfast in Northern Ireland. Our research interests are focused primarily on computational and theoretical physics.
The Old Physics Building,
CTAMOP is situated.