Taking gradients through experiments: LSTMs and memory proximal policy optimization for black-box quantum control

Understand

In this work we introduce the application of black-box quantum control as an interesting rein- forcement learning problem to the machine learning community.

• We analyze the structure of the reinforcement learning problems arising in quantum physics and argue that agents parameterized by long short-term memory (LSTM) networks trained via stochastic policy gradients yield a general method to solving them.
• In this context we introduce a variant of the proximal policy optimization (PPO) algorithm called the memory proximal policy optimization (MPPO) which is based on this analysis.
• We then show how it can be applied to specific learning tasks and present results of nu- merical experiments showing that our method achieves state-of-the-art results for several learning tasks in quantum control with discrete and continouous control parameters.