Abstract
Reinforcement learning (RL) enables agents to learn optimal policies through environmental interaction. However, RL suffers from reduced learning efficiency due to the curse of dimensionality in high-dimensional spaces. Quantum reinforcement learning (QRL) addresses this issue by leveraging superposition and entanglement in quantum computing, allowing efficient handling of high-dimensional problems with fewer resources. QRL combines quantum neural networks (QNNs) with RL, where the parameterized quantum circuit (PQC) acts as the core computational module. The PQC performs linear and nonlinear transformations through gate operations, similar to hidden layers in classical neural networks. Previous QRL studies, however, have used fixed PQC structures based on empirical intuition without verifying their optimality. This paper proposes a QRL-NAS algorithm that integrates quantum neural architecture search (QNAS) to optimize PQC structures within QRL. Experiments demonstrate that QRL-NAS achieves higher rewards than QRL with fixed circuits, validating its effectiveness and practical utility.
Abstract (translated)
强化学习（RL）使智能体能够通过与环境的交互来学习最优策略。然而，由于高维空间中的维度灾难问题，强化学习的学习效率降低了。量子强化学习（QRL）通过利用量子计算中的叠加和纠缠来解决这一问题，从而能够在较少资源的情况下有效地处理高维问题。QRL结合了量子神经网络（QNN）与传统强化学习，其中参数化量子电路（PQC）作为核心计算模块。PQC通过门操作执行线性和非线性变换，类似于经典神经网络中的隐藏层。 然而，此前的QRL研究使用的是基于经验直觉而非经过验证的最佳结构的固定PQC架构。本文提出了一种名为QRL-NAS的算法，该算法将量子神经架构搜索（QNAS）集成到QRL中，以优化PQC结构。实验结果表明，相较于采用固定电路的QRL，QRL-NAS实现了更高的奖励值，验证了其有效性和实用价值。 通过这样的方式，QRL-NAS不仅提升了强化学习在处理高维度问题上的效率和性能，同时也展示了量子计算技术在人工智能领域中的巨大潜力。
URL
https://arxiv.org/abs/2507.00589
