论文标题
连续动作空间中的量子加固学习
Quantum reinforcement learning in continuous action space
论文作者
论文摘要
量子增强学习（QRL）是近期量子设备的有希望的范式。尽管现有的QRL方法在离散的动作空间中显示出成功，但由于离散化引入了维数的诅咒，将这些技术扩展到连续域是一项挑战。为了克服这一限制，我们引入了一种量子深层确定性策略梯度（DDPG）算法，该算法有效地解决了连续动作空间中的经典和量子顺序决策问题。此外，我们的方法有助于单次量子状态生成：一次性优化产生一个模型，该模型输出了将固定初始状态驱动到任何所需目标状态所需的控制顺序。相比之下，常规量子控制方法要求对每个目标状态进行单独的优化。我们通过模拟证明了方法的有效性，并讨论了其在量子控制中的潜在应用。
Quantum reinforcement learning (QRL) is a promising paradigm for near-term quantum devices. While existing QRL methods have shown success in discrete action spaces, extending these techniques to continuous domains is challenging due to the curse of dimensionality introduced by discretization. To overcome this limitation, we introduce a quantum Deep Deterministic Policy Gradient (DDPG) algorithm that efficiently addresses both classical and quantum sequential decision problems in continuous action spaces. Moreover, our approach facilitates single-shot quantum state generation: a one-time optimization produces a model that outputs the control sequence required to drive a fixed initial state to any desired target state. In contrast, conventional quantum control methods demand separate optimization for each target state. We demonstrate the effectiveness of our method through simulations and discuss its potential applications in quantum control.
