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Experimentally realizing efficient quantum control with reinforcement learning
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DOI：10.1007/s11433-021-1841-2.png)
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We experimentally investigate deep reinforcement learning (DRL) as an artificial intelligence approach to control a quantum system. We verify that DRL explores fast and robust digital quantum controls with operation time analytically hinted by shortcuts to adiabaticity. In particular, the protocol's robustness against both over-rotations and off-resonance errors can still be achieved simultaneously without any priori input. For the thorough comparison, we choose the task as single-qubit flipping, in which various analytical methods are well-developed as the benchmark, ensuring their feasibility in the quantum system as well. Consequently, a gate operation is demonstrated on a trapped Yb-171(+) ion, significantly outperforming analytical pulses in the gate time and energy cost with hybrid robustness, as well as the fidelity after repetitive operations under time-varying stochastic errors. Our experiments reveal a framework of computer-inspired quantum control, which can be extended to other complicated tasks without loss of generality.
Keyword:
quantum control
reinforcement learning
trapped ion
quantum computing
noise robustness
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7.5
论文数:
3.9K
被引数:
7.4K
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