Experimentally Realizing Efficient Quantum Control with Reinforcement Learning

• URL: http://arxiv.org/abs/2101.09020v1
• Date: Fri, 22 Jan 2021 09:34:58 GMT
• Title: Experimentally Realizing Efficient Quantum Control with Reinforcement Learning
• Authors: Ming-Zhong Ai, Yongcheng Ding, Yue Ban, Jos\'e D. Mart\'in-Guerrero, Jorge Casanova, Jin-Ming Cui, Yun-Feng Huang, Xi Chen, Chuan-Feng Li, Guang-Can Guo
• Abstract summary: We experimentally demonstrate an alternative approach to quantum control based on deep reinforcement learning (DRL) on a trapped $171mathrmYb+$ ion. In particular, we find that DRL leads to fast and robust digital quantum operations with running time bounded by shortcuts to adiabaticity (STA) Our experiments reveal a general framework of digital quantum control, leading to a promising enhancement in quantum information processing.
• Score: 2.733342606024131
• License: http://creativecommons.org/licenses/by/4.0/
• 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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