In the noisy intermediate-scale quantum era, optimal digitized pulses are requisite for efficient quantum control. This goal is translated into dynamic programming, in which a deep reinforcement learning (DRL) agent is gifted. As a reference, shortcuts to adiabaticity (STA) provide analytical approaches to adiabatic speedup by pulse control. Here, we select the single-component control of qubits, resembling the ubiquitous two-level Landau-Zener problem for gate operation. We aim at obtaining fast and robust digital pulses by combining the STA and DRL algorithm. In particular, we find that DRL leads to robust digital quantum control with the operation time bounded by quantum speed limits dictated by STA. In addition, we demonstrate that robustness against systematic errors can be achieved by DRL without any input from STA. Our results introduce a general framework of digital quantum control, leading to a promising enhancement in quantum information processing.

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• Adiabatic quantum optimization
• Machine learning
• Quantum control
• Adiabatic approximation
• Two-level models