论文标题
通过增强学习对多级耗散量子系统的量子最佳控制
Quantum optimal control of multi-level dissipative quantum systems with Reinforcement Learning
论文作者
论文摘要
高精度对复杂量子系统进行操纵和控制对于实现通用容错量子计算至关重要。 For a physical system with restricted control resources, it is a challenge to control the dynamics of the target system efficiently and precisely under disturbances.在这里，我们提出了一个多级耗散量子控制框架，并表明深钢筋学习提供了一种有效的方法，可以通过复杂量子系统的受限控制参数来识别最佳策略。可以将该框架推广到其他量子控制模型。与传统的最佳控制方法相比，这种深厚的增强学习算法可以实现具有不同类型干扰的多级量子系统的有效和精确控制。
Manipulate and control of the complex quantum system with high precision are essential for achieving universal fault tolerant quantum computing. For a physical system with restricted control resources, it is a challenge to control the dynamics of the target system efficiently and precisely under disturbances. Here we propose a multi-level dissipative quantum control framework and show that deep reinforcement learning provides an efficient way to identify the optimal strategies with restricted control parameters of the complex quantum system. This framework can be generalized to be applied to other quantum control models. Compared with the traditional optimal control method, this deep reinforcement learning algorithm can realize efficient and precise control for multi-level quantum systems with different types of disturbances.
