Learning quantum states is a crucial task for realizing the potential of quantum information technology. Recently, neural approaches have emerged as promising methods for learning quantum states. We propose a meta-learning model that employs reinforcement learning (RL) to optimize the process of learning quantum states. For learning quantum states, our scheme trains a Hardware efficient ansatz with a blackbox optimization algorithm, called evolution strategy (ES). To enhance the efficiency of ES, a RL agent dynamically adjusts the hyperparameters of ES. To facilitate the RL training, we introduce an action repetition strategy inspired by curriculum learning. The RL agent significantly improves the sample efficiency of learning random quantum states, and achieves infidelity scaling close to the Heisenberg limit. We showcase that the RL agent trained using 3-qubit states can be generalized to learning up to 5-qubit states. These results highlight the utility of RL-driven meta-learning to enhance the efficiency and generalizability of learning quantum states. Our approach can be applicable to improve quantum control, quantum optimization, and quantum machine learning.

翻译：量子态学习是实现量子信息技术潜力的关键任务。近年来，神经网络方法已成为学习量子态的有前景方法。我们提出一种元学习模型，该模型采用强化学习（RL）来优化量子态学习过程。对于量子态学习，我们的方案使用一种称为进化策略（ES）的黑盒优化算法来训练硬件高效拟设。为提升ES的效率，RL智能体动态调整ES的超参数。为促进RL训练，我们引入一种受课程学习启发的动作重复策略。该RL智能体显著提高了学习随机量子态的样本效率，并实现了接近海森堡极限的保真度缩放。我们展示了使用3量子比特态训练的RL智能体可推广至学习多达5量子比特态。这些结果凸显了RL驱动的元学习在提升量子态学习效率与泛化能力方面的实用性。我们的方法可适用于改进量子控制、量子优化及量子机器学习。