  Reinforcement learning has been widely used in many problems, including
quantum control of qubits. However, such problems can, at the same time, be
solved by traditional, non-machine-learning methods, such as stochastic
gradient descent and Krotov algorithms, and it remains unclear which one is
most suitable when the control has specific constraints. In this work, we
perform a comparative study on the efficacy of three reinforcement learning
algorithms: tabular Q-learning, deep Q-learning, and policy gradient, as well
as two non-machine-learning methods: stochastic gradient descent and Krotov
algorithms, in the problem of preparing a desired quantum state. We found that
overall, the deep Q-learning and policy gradient algorithms outperform others
when the problem is discretized, e.g. allowing discrete values of control, and
when the problem scales up. The reinforcement learning algorithms can also
adaptively reduce the complexity of the control sequences, shortening the
operation time and improving the fidelity. Our comparison provides insights
into the suitability of reinforcement learning in quantum control problems.
