A Quantum States Preparation Method Based on Difference-Driven Reinforcement Learning
This work addresses a domain-specific problem for quantum computing researchers, offering incremental improvements in quantum state preparation methods.
The authors tackled the problem of slow convergence and low fidelity in preparing target quantum states for two-qubit systems by proposing a difference-driven reinforcement learning algorithm with an improved reward function and action selection strategy, resulting in improved convergence speed and fidelity under limited conditions.
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.