Deep reinforcement learning for universal quantum state preparation via dynamic pulse control.

Accurate and efficient preparation of quantum states is a core issue in building a quantum computer. In this paper, we propose a scheme to prepare a certain single or two-qubit state from an arbitrary initial state in semiconductor double quantum dots. With the aid of deep reinforcement learning the suitable exchange couplings between electrons can be obtained via automatically designing electric pulses. The universal advantage of our scheme is that once the network is trained for the target state, it can be used for an arbitrary initial state and repeated retraining of the network for new initial states is avoided. Furthermore, we find that our scheme is robust against static and dynamic fluctuations, such as charge and nuclear noises.

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