A differentiable programming method for quantum control

Understand

Optimal control is highly desirable in many current quantum systems, especially to realize tasks in quantum information processing.

• We introduce a method based on differentiable programming to leverage explicit knowledge of the differential equations governing the dynamics of the system.
• In particular, a control agent is represented as a neural network that maps the state of the system at a given time to a control pulse.
• The parameters of this agent are optimized via gradient information obtained by direct differentiation through both the neural network \emph{and} the differential equation of the system.