CUED Publications database

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

August, M and Hernández-Lobato, JM Taking gradients through experiments: LSTMs and memory proximal policy optimization for black-box quantum control. In: International Conference on High Performance Computing 2018, 2018-6-24 to 2018-6-28, Frankfurt pp. 591-693.. (Unpublished)

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Abstract

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.

Item Type: Conference or Workshop Item (UNSPECIFIED)
Uncontrolled Keywords: cs.LG cs.LG quant-ph
Subjects: UNSPECIFIED
Divisions: Div F > Computational and Biological Learning
Depositing User: Cron Job
Date Deposited: 17 Oct 2018 20:05
Last Modified: 15 Sep 2020 04:27
DOI: 10.1007/978-3-030-02465-9_43