  We propose and analyze the use of Bayesian optimization techniques to design
quantum annealing schedules with minimal user and resource requirements. We
showcase our scheme with results for two paradigmatic spin models. We find that
Bayesian optimization is able to identify schedules resulting in fidelities
several orders of magnitude better than standard protocols for both quantum and
reverse annealing, as applied to the $p$-spin model. We also show that our
scheme can help improve the design of hybrid quantum algorithms for hard
combinatorial optimization problems, such as the maximum independent set
problem, and illustrate these results via experiments on a neutral atom quantum
processor available on Amazon Braket.
