Quantum reinforcement learning in continuous action space
This work addresses the limitation of existing quantum reinforcement learning methods to discrete action spaces, offering a solution for researchers and practitioners working with continuous quantum control problems.
This paper introduces a quantum Deep Deterministic Policy Gradient (DDPG) algorithm to address quantum reinforcement learning problems in continuous action spaces, a challenge for existing QRL methods. The method also enables single-shot quantum state generation, where a single optimization produces a model capable of generating control sequences for any desired target state, unlike conventional methods requiring separate optimization per target.
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.