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

Abstract

In this work we introduce the application of black-box quantum control as an interesting reinforcement 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… (More)

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