论文标题
通过动态脉冲控制进行通用量子状态制备的深度加固学习
Deep reinforcement learning for universal quantum state preparation via dynamic pulse control
论文作者
论文摘要
准确有效的量子状态准备是构建量子计算机的核心问题。在本文中，我们研究了如何在深度加固学习的帮助下从半导体双量子点中的任意初始状态制备特定的单一或二量目标状态。我们的方法基于对许多准备任务的网络培训。结果表明，一旦网络经过良好的训练，它就适用于连续希尔伯特空间中的任何初始状态。因此，避免了重复进行新准备任务的培训。我们的计划的表现优于基于梯度的传统优化方法，其设计效率较高，并且在离散控制空间中的准备质量。此外，我们发现我们计划设计的控制轨迹对静态和动态波动（例如电荷和核噪声）具有鲁棒性。
Accurate and efficient preparation of quantum state is a core issue in building a quantum computer. In this paper, we investigate how to prepare a certain single- or two-qubit target state from arbitrary initial states in semiconductor double quantum dots with the aid of deep reinforcement learning. Our method is based on the training of the network over numerous preparing tasks. The results show that once the network is well trained, it works for any initial states in the continuous Hilbert space. Thus repeated training for new preparation tasks is avoided. Our scheme outperforms the traditional optimization approaches based on gradient with both the higher designing efficiency and the preparation quality in discrete control space. Moreover, we find that the control trajectories designed by our scheme are robust against static and dynamic fluctuations, such as charge and nuclear noises.
