用量子几何方法优化无线链路自适应，提升通信效率与稳定性。
QPPG: Quantum-Preconditioned Policy Gradient for Link Adaptation in Rayleigh Fading Channels
- 引入量子几何预处理，稳定策略梯度更新
- 吞吐量提升28.6%，发射功率降低43.8%
- 适合6G智能无线网络中的鲁棒自适应设计
在动态衰落环境中，可靠的链路自适应对高效无线通信至关重要。然而，强化学习方法常因策略梯度条件不佳导致收敛不稳定，限制了实际应用。本文提出量子预处理策略梯度（QPPG）算法，利用基于Fisher信息的预处理机制，稳定并加速策略更新。在瑞利衰落场景下的评估表明，相比经典方法，QPPG实现更快收敛，平均吞吐量提升28.6%，平均发射功率降低43.8%。该工作首次将量子几何条件化引入链路自适应，为未来6G网络中鲁棒、量子启发的强化学习发展提供了重要进展，显著提升通信可靠性与能效。
原文摘要 · Abstract (English)
Reliable link adaptation is critical for efficient wireless communications in dynamic fading environments. However, reinforcement learning (RL) solutions often suffer from unstable convergence due to poorly conditioned policy gradients, hindering their practical application. We propose the quantum-preconditioned policy gradient (QPPG) algorithm, which leverages Fisher-information-based preconditioning to stabilise and accelerate policy updates. Evaluations in Rayleigh fading scenarios show that QPPG achieves faster convergence, a 28.6% increase in average throughput, and a 43.8% decrease in average transmit power compared to classical methods. This work introduces quantum-geometric conditioning to link adaptation, marking a significant advance in developing robust, quantum-inspired reinforcement learning for future 6G networks, thereby enhancing communication reliability and energy efficiency.
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