The Quantum Cartpole: A benchmark environment for non-linear reinforcement learning

Understand

Feedback-based control is the de-facto standard when it comes to controlling classical stochastic systems and processes.

• However, standard feedback-based control methods are challenged by quantum systems due to measurement induced backaction and partial observability.
• Here we remedy this by using weak quantum measurements and model-free reinforcement learning agents to perform quantum control.
• By comparing control algorithms with and without state estimators to stabilize a quantum particle in an unstable state near a local potential energy maximum, we show how a trade-off between state estimation and controllability arises.