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

12 Feb 2018Moritz AugustJosé Miguel Hernández-Lobato

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... (read more)

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