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

TL;DR

This paper presents a deep reinforcement learning approach to efficiently and robustly prepare quantum states in semiconductor quantum dots, outperforming traditional methods and generalizing across initial states.

Contribution

It introduces a trained neural network that can prepare quantum states from any initial state without retraining, enhancing efficiency and robustness in quantum control.

Findings

The method outperforms traditional gradient-based optimization in efficiency and quality.

The trained network generalizes to any initial state in the Hilbert space.

Control trajectories are robust against noise and fluctuations.

Abstract

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.