A Quantum States Preparation Method Based on Difference-Driven Reinforcement Learning

TL;DR

This paper introduces a difference-driven reinforcement learning method to efficiently prepare high-fidelity two-qubit quantum states, overcoming slow convergence issues of previous methods by improving reward functions and action strategies.

Contribution

It proposes a novel RL algorithm with a weighted reward function and adaptive action strategy tailored for two-qubit quantum state preparation, enhancing convergence speed and fidelity.

Findings

Faster convergence compared to existing algorithms.

Higher fidelity of prepared quantum states.

Effective under limited operational conditions.

Abstract

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.