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Open Quantum Systems Decoherence Quantum Machine Learning

Deep Reinforcement Learning Control of Quantum Cartpoles

arXiv

Authors: Zhikang T. Wang, Yuto Ashida, Masahito Ueda

Year

2019

Paper ID

15424

Status

Preprint

Abstract Read

~2 min

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Abstract

We generalize a standard benchmark of reinforcement learning, the classical cartpole balancing problem, to the quantum regime by stabilizing a particle in an unstable potential through measurement and feedback. We use state-of-the-art deep reinforcement learning to stabilize a quantum cartpole and find that our deep learning approach performs comparably to or better than other strategies in standard control theory. Our approach also applies to measurement-feedback cooling of quantum oscillators, showing the applicability of deep learning to general continuous-space quantum control.

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This paper contributes to the Quantum Machine Learning research area in the Quantum Articles archive.
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We generalize a standard benchmark of reinforcement learning, the classical cartpole balancing problem, to the quantum regime by stabilizing a particle in an unstable potential...

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References & Citation Signals

[1] DOI https://doi.org/arXiv:1910.09200 [2] arXiv https://arxiv.org/abs/1910.09200 [3] Publisher https://arxiv.org/abs/1910.09200

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