Experimentally realizing efficient quantum control with reinforcement learning

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 ¹⁷¹Yb⁺ 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.