Borah Sangkha, Sarma Bijita, Kewming Michael, Milburn Gerard J, Twamley Jason
Quantum Machines Unit, Okinawa Institute of Science and Technology Graduate University, Onna-son, Okinawa 904-0495, Japan.
Centre for Engineered Quantum Systems, School of Mathematics and Physics, University of Queensland, Brisbane, Queensland, 4072 Australia.
Phys Rev Lett. 2021 Nov 5;127(19):190403. doi: 10.1103/PhysRevLett.127.190403.
Closed loop quantum control uses measurement to control the dynamics of a quantum system to achieve either a desired target state or target dynamics. In the case when the quantum Hamiltonian is quadratic in x and p, there are known optimal control techniques to drive the dynamics toward particular states, e.g., the ground state. However, for nonlinear Hamiltonian such control techniques often fail. We apply deep reinforcement learning (DRL), where an artificial neural agent explores and learns to control the quantum evolution of a highly nonlinear system (double well), driving the system toward the ground state with high fidelity. We consider a DRL strategy which is particularly motivated by experiment where the quantum system is continuously but weakly measured. This measurement is then fed back to the neural agent and used for training. We show that the DRL can effectively learn counterintuitive strategies to cool the system to a nearly pure "cat" state, which has a high overlap fidelity with the true ground state.
闭环量子控制利用测量来控制量子系统的动力学，以实现期望的目标状态或目标动力学。在量子哈密顿量在x和p上是二次型的情况下，存在已知的最优控制技术来驱动动力学朝向特定状态，例如基态。然而，对于非线性哈密顿量，此类控制技术常常失效。我们应用深度强化学习（DRL），其中一个人工神经智能体探索并学习控制一个高度非线性系统（双阱）的量子演化，以高保真度将系统驱动至基态。我们考虑一种特别受实验启发的DRL策略，在该实验中量子系统被连续但微弱地测量。然后此测量被反馈给神经智能体并用于训练。我们表明DRL能够有效地学习反直觉策略，将系统冷却至几乎纯的“猫”态，该态与真实基态具有高重叠保真度。
