混合量子-经典策略梯度用于网络物理系统的自适应控制：VQC与MLP的比较研究
Hybrid Quantum-Classical Policy Gradient for Adaptive Control of Cyber-Physical Systems: A Comparative Study of VQC vs. MLP
AI总结：
本研究比较了经典MLP与量子VQC在CartPole-v1控制任务中的强化学习性能，发现MLP收敛更优且抗噪更强，而VQC参数更少，凸显量子架构在硬件改进后的效率潜力。
摘要：
本研究对经典强化学习（QRL）与量子强化学习（QRL）范式进行了比较评估，以探究它们在基准控制环境中的收敛行为、观测噪声下的鲁棒性以及计算效率。研究采用多层感知器（MLP）智能体作为经典基线，参数化变分量子电路（VQC）作为量子对应物，两者均在CartPole-v1环境中训练了500个回合。实证结果表明，经典MLP实现了近乎最优的策略收敛，平均回报为498.7±3.2，并在整个训练过程中保持稳定平衡。相比之下，VQC表现出有限的学习能力，平均回报仅为14.6±4.8，主要受限于电路深度和量子比特连接性。噪声鲁棒性分析进一步揭示，MLP策略在高斯扰动下性能优雅地退化，而VQC在相同噪声水平下表现出更高的敏感性。尽管渐近性能较低，但VQC具有显著更少的参数数量和略微增加的训练时间，突显了其在低资源量子处理器上的可扩展性潜力。结果表明，尽管经典神经策略在当前控制基准中仍占主导地位，但一旦硬件噪声和表达能力限制得到缓解，量子增强架构可能提供有前景的效率优势。
英文摘要：
The comparative evaluation between classical and quantum reinforcement learning (QRL) paradigms was conducted to investigate their convergence behavior, robustness under observational noise, and computational efficiency in a benchmark control environment. The study employed a multilayer perceptron (MLP) agent as a classical baseline and a parameterized variational quantum circuit (VQC) as a quantum counterpart, both trained on the CartPole-v1 environment over 500 episodes. Empirical results demonstrated that the classical MLP achieved near-optimal policy convergence with a mean return of 498.7 +/- 3.2, maintaining stable equilibrium throughout training. In contrast, the VQC exhibited limited learning capability, with an average return of 14.6 +/- 4.8, primarily constrained by circuit depth and qubit connectivity. Noise robustness analysis further revealed that the MLP policy deteriorated gracefully under Gaussian perturbations, while the VQC displayed higher sensitivity at equivalent noise levels. Despite the lower asymptotic performance, the VQC exhibited significantly lower parameter count and marginally increased training time, highlighting its potential scalability for low-resource quantum processors. The results suggest that while classical neural policies remain dominant in current control benchmarks, quantum-enhanced architectures could offer promising efficiency advantages once hardware noise and expressivity limitations are mitigated.
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