Recurrent Neural Networks for Quantum Feedback

Neural networks have become powerful tools to tackle complex problems in real-world applications and have recently attracted increasing attention from various fields. Recurrent network architectures (like long short-term memory) provide an incorporated memory that make them suitable to also deal with situations where knowledge about the true state of a system is only collected over time. We apply this to the search for optimal control sequences in quantum feedback, where actions (the application of quantum gates) should be chosen solely based on previous actions and measurement outcomes, i.e., without knowledge of the full quantum state. We have investigated how to combine reinforcement learning techniques with recurrent neural networks in order to train an agent to preserve an arbitrary quantum state by choosing actions from a set of available quantum gates.