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 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.

中文翻译：

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量子控制的微分编程方法

在许多当前的量子系统中，特别是为了实现量子信息处理中的任务，最优控制是非常需要的。我们介绍一种基于微分程序的方法，以利用对控制系统动力学的微分方程的显式知识。特别地，控制代理表示为将给定时间的系统状态映射到控制脉冲的神经网络。通过通过神经网络和系统的微分方程直接微分获得的梯度信息，可以优化该代理的参数。这种完全可区分的强化学习方法最终会产生时间相关的控制参数，从而优化所需的品质因数。