  Quantum reinforcement learning (QRL) is one promising algorithm proposed for
near-term quantum devices. Early QRL proposals are effective at solving
problems in discrete action space, but often suffer from the curse of
dimensionality in the continuous domain due to discretization. To address this
problem, we propose a quantum Deep Deterministic Policy Gradient algorithm that
is efficient at solving both classical and quantum sequential decision problems
in the continuous domain. As an application, our method can solve the quantum
state-generation problem in a single shot: it only requires a one-shot
optimization to generate a model that outputs the desired control sequence for
arbitrary target state. In comparison, the standard quantum control method
requires optimizing for each target state. Moreover, our method can also be
used to physically reconstruct an unknown quantum state.
