Science Daily October 25, 2018
Researchers in Germany show how a network-based “agent” can discover complete quantum-error-correction strategies, protecting a collection of qubits against noise. These strategies require feedback adapted to measurement outcomes. To find strategies without human intervention they developed two-stage learning with teacher and student networks and a reward quantifying the capability to recover the quantum information stored in a multiqubit system. Beyond its immediate impact on quantum computation, the work more generally demonstrates the promise of neural-network-based reinforcement learning in physics… read more. TECHNICAL ARTICLE
The image visualizes the activity of artificial neurons in the Erlangen researchers’ neural network while it is solving its task. Credit: Max Planck Institute for the Science of Light