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

EPJ Quantum Technology

Jing Wu¹  Zhao-Ming Wang¹  Jia-Hui Zhang¹  Rui Wang¹  Shen-Shuang Nie¹  Run-Hong He¹ 

[1] College of Physics and Optoelectronic Engineering, Ocean University of China, Qingdao, China;

关键词: Quantum control;    Quantum state preparation;    Semiconductor double quantum dots;    Deep reinforcement learning;   

DOI  :  10.1140/epjqt/s40507-021-00119-6

来源: Springer

【 摘 要 】

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 only a few discrete control pulses by leveraging the 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 efficiency and the preparation quality in discrete control space. Moreover, we find that the control trajectories designed by our scheme are robust against stochastic fluctuations within certain thresholds, such as the charge and nuclear noises.

【 授权许可】

CC BY