用自动搜索优化量子强化学习的电路结构，提升学习效率。
Quantum Circuit Structure Optimization for Quantum Reinforcement Learning
- 引入量子神经架构搜索，自动寻找最优量子电路结构
- 在测试环境中实现更高奖励，优于固定结构的量子强化学习
- 适合研究量子机器学习与智能控制的科研人员
强化学习（RL）使智能体通过与环境交互学习最优策略，但在高维空间中易受维度灾难影响，导致学习效率下降。量子强化学习（QRL）利用量子计算中的叠加与纠缠特性，以更少资源高效处理高维问题。QRL结合量子神经网络（QNN）与RL，其中参数化量子电路（PQC）作为核心计算模块，通过门操作实现线性与非线性变换，类似经典神经网络的隐藏层。然而，以往研究多采用基于经验直觉的固定PQC结构，未验证其最优性。本文提出QRL-NAS算法，将量子神经架构搜索（QNAS）融入QRL，自动优化PQC结构。实验表明，QRL-NAS在多个基准任务中获得更高奖励，验证了其有效性和实用性。
原文摘要 · Abstract (English)
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.
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