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arXiv · 2101.09020

Experimentally Realizing Efficient Quantum Control with Reinforcement Learning

Ming-Zhong Ai·Yongcheng Ding·Yue Ban·José D. Martín-Guerrero·Jorge Casanova·Jin-Ming Cui·Yun-Feng Huang·Xi Chen·Chuan-Feng Li·Guang-Can Guo
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Abstract

Robust and high-precision quantum control is crucial but challenging for scalable quantum computation and quantum information processing. Traditional adiabatic control suffers severe limitations on gate performance imposed by environmentally induced noise because of a quantum system's limited coherence time. In this work, we experimentally demonstrate an alternative approach {to quantum control} based on deep reinforcement learning (DRL) on a trapped $^{171}\mathrm{Yb}^{+}$ ion. In particular, we find that DRL leads to fast and robust {digital quantum operations with running time bounded by shortcuts to adiabaticity} (STA). Besides, we demonstrate that DRL's robustness against both Rabi and detuning errors can be achieved simultaneously without any input from STA. Our experiments reveal a general framework of digital quantum control, leading to a promising enhancement in quantum information processing.

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Ming-Zhong Ai, Yongcheng Ding, Yue Ban, José D. Martín-Guerrero, Jorge Casanova, Jin-Ming Cui, Yun-Feng Huang, Xi Chen, Chuan-Feng Li, Guang-Can Guo. 2021-01-22. Experimentally Realizing Efficient Quantum Control with Reinforcement Learning. https://arxiv.org/abs/2101.09020

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