When does reinforcement learning stand out in quantum control? A comparative study on state preparation

Understand

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.