用强化学习提升量子态学习效率，逼近理论极限。
Reinforcement learning to learn quantum states for Heisenberg scaling accuracy
- 通过元学习与强化学习结合，优化量子态训练过程。
- 在随机量子态上实现接近海森堡极限的保真度误差。
- 3比特训练模型可泛化至5比特系统，提升通用性。
量子态学习是实现量子信息技术的关键任务。近期，神经方法作为学习量子态的有前途手段崭露头角。本文提出一种基于元学习的强化学习（RL）模型，用于优化量子态学习流程。为提高RL的数据效率，引入受课程学习启发的动作重复策略。该RL代理显著提升了随机量子态学习的样本效率，并实现了接近海森堡极限的保真度误差。此外，使用3比特量子态训练的RL代理可泛化至最多5比特量子态的学习。这些结果表明，强化学习驱动的元学习能有效提升量子态学习的效率与泛化能力。本方法可应用于改善量子控制、量子优化和量子机器学习。
原文摘要 · Abstract (English)
Learning quantum states is a crucial task for realizing quantum information technology. Recently, neural approaches have emerged as promising methods for learning quantum states. We propose a meta-learning model that utilizes reinforcement learning (RL) to optimize the process of learning quantum states. To improve the data efficiency of the RL, 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 also show that the RL agent trained using 3-qubit states can generalize 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 applied to improve quantum control, quantum optimization, and quantum machine learning.
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