  In the realm of quantum control, reinforcement learning, a prominent branch
of machine learning, emerges as a competitive candidate for computer-assisted
optimal design for experiments. This study investigates the extent to which
guidance from human experts is necessary for the effective implementation of
reinforcement learning in designing quantum control protocols. Specifically, we
focus on the engineering of a robust two-qubit gate within a nuclear magnetic
resonance system, utilizing a combination of analytical solutions as prior
knowledge and techniques from the field of computer science. Through extensive
benchmarking of different models, we identify dropout, a widely-used method for
mitigating overfitting in machine learning, as an especially robust approach.
Our findings demonstrate the potential of incorporating computer science
concepts to propel the development of advanced quantum technologies.
