论文标题
量子控制的可区分编程方法
A differentiable programming method for quantum control
论文作者
论文摘要
在许多当前的量子系统中，最佳控制是非常可取的，尤其是实现量子信息处理中的任务。我们介绍了一种基于可区分编程的方法，以利用管理系统动力学的微分方程的明确知识。特别是，控制代理表示为神经网络，该神经网络在给定时间将系统状态映射到控制脉冲。通过通过神经网络\ emph {and}直接分化获得的梯度信息，通过梯度信息进行了优化。这种完全可区分的增强学习方法最终产生了与时间相关的控制参数，以优化所需的功绩图。我们证明了三个系统的本征态准备任务中方法对噪声的可行性和鲁棒性：一个〜单量子，〜量子链和量子参数振荡器。
Optimal control is highly desirable in many current quantum systems, especially to realize tasks in quantum information processing. We introduce a method based on differentiable programming to leverage explicit knowledge of the differential equations governing the dynamics of the system. In particular, a control agent is represented as a neural network that maps the state of the system at a given time to a control pulse. The parameters of this agent are optimized via gradient information obtained by direct differentiation through both the neural network \emph{and} the differential equation of the system. This fully differentiable reinforcement learning approach ultimately yields time-dependent control parameters optimizing a desired figure of merit. We demonstrate the method's viability and robustness to noise in eigenstate preparation tasks for three systems: a~single qubit, a~chain of qubits, and a quantum parametric oscillator.
