  Due to the large state space of the two-qubit system, and the adoption of
ladder reward function in the existing quantum state preparation methods, the
convergence speed is slow and it is difficult to prepare the desired target
quantum state with high fidelity under limited conditions. To solve the above
problems, a difference-driven reinforcement learning (RL) algorithm for quantum
state preparation of two-qubit system is proposed by improving the reward
function and action selection strategy. Firstly, a model is constructed for the
problem of preparing quantum states of a two-qubit system, with restrictions on
the type of quantum gates and the time for quantum state evolution. In the
preparation process, a weighted differential dynamic reward function is
designed to assist the algorithm quickly obtain the maximum expected cumulative
reward. Then, an adaptive e-greedy action selection strategy is adopted to
achieve a balance between exploration and utilization to a certain extent,
thereby improving the fidelity of the final quantum state. The simulation
results show that the proposed algorithm can prepare quantum state with high
fidelity under limited conditions. Compared with other algorithms, it has
different degrees of improvement in convergence speed and fidelity of the final
quantum state.

Quantum Physics;Artificial Intelligence;Emerging Technologies;Machine Learning