Quantum reinforcement learning in continuous action space
- URL: http://arxiv.org/abs/2012.10711v2
- Date: Wed, 13 Jan 2021 15:58:16 GMT
- Title: Quantum reinforcement learning in continuous action space
- Authors: Shaojun Wu, Shan Jin, Dingding Wen, Xiaoting Wang
- Abstract summary: We propose a quantum version of the Deep Deterministic Policy Gradient method constructed from quantum neural networks.
As applications, we demonstrate that quantum control tasks, including the eigenvalue problem and quantum state generation, can be formulated as sequential decision problems.
- Score: 0.5276232626689568
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Quantum mechanics has the potential to speedup machine learning algorithms,
including reinforcement learning(RL). Previous works have shown that quantum
algorithms can efficiently solve RL problems in discrete action space, but
could become intractable in continuous domain, suffering notably from the curse
of dimensionality due to discretization. In this work, we propose an
alternative quantum circuit design that can solve RL problems in continuous
action space without the dimensionality problem. Specifically, we propose a
quantum version of the Deep Deterministic Policy Gradient method constructed
from quantum neural networks, with the potential advantage of obtaining an
exponential speedup in gate complexity for each iteration. As applications, we
demonstrate that quantum control tasks, including the eigenvalue problem and
quantum state generation, can be formulated as sequential decision problems and
solved by our method.
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