Polar molecules are a promising platform for achieving
scalable
quantum information processing because of their long-range electric
dipole–dipole interactions. Here, we take the coupled ultracold
CaF molecules in an external electric field with gradient as qubits
and concentrate on the creation of intermolecular entanglement with
the method of deep reinforcement learning (RL). After sufficient training
episodes, the educated RL agents can discover optimal time-dependent
control fields that steer the molecular systems from separate states
to two-qubit and three-qubit entangled states with high fidelities.
We analyze the fidelities and the negativities (characterizing entanglement)
of the generated states as a function of training episodes. Moreover,
we present the population dynamics of the molecular systems under
the influence of control fields discovered by the agents. Compared
with the schemes for creating molecular entangled states based on
optimal control theory, some conditions (e.g., molecular spacing and
electric field gradient) adopted in this work are more feasible in
the experiment. Our results demonstrate the potential of machine learning
to effectively solve quantum control problems in polar molecular systems.