  The preparation of quantum states is essential in the realm of quantum
information processing, and the development of efficient methodologies can
significantly alleviate the strain on quantum resources. Within the framework
of deep reinforcement learning (DRL), we integrate the initial and the target
state information within the state preparation task together, so as to realize
the control trajectory design between two arbitrary quantum states. Utilizing a
semiconductor double quantum dots (DQDs) model, our results demonstrate that
the resulting control trajectories can effectively achieve arbitrary quantum
state preparation (AQSP) for both single-qubit and two-qubit systems, with
average fidelities of 0.9868 and 0.9556 for the test sets, respectively.
Furthermore, we consider the noise around the system and the control
trajectories exhibit commendable robustness against charge and nuclear noise.
Our study not only substantiates the efficacy of DRL in QSP, but also provides
a new solution for quantum control tasks of multi-initial and multi-objective
states, and is expected to be extended to a wider range of quantum control
problems.
