mlsanityData to 14 Oct 2026

QPPG: Quantum-Preconditioned Policy Gradient for Link Adaptation in Rayleigh Fading Channels

arXiv 2506.15753 ↗First posted 18 Jun 2025quant-ph cs.LG cs.SYcited by 0

On the map: Deep reinforcement learning methods → Quantum reinforcement learning

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Quantum reinforcement learning, one of 15 directions in Deep reinforcement learning methods. Builds on 4 of its 17 references that are in the map. First posted on arXiv 18 Jun 2025.

Builds on

4 of 17 references.

References · 4

  1. 2018Barren plateaus in quantum neural network training landscapescited by 630
  2. 2019A Vision of 6G Wireless Systems: Applications, Trends, Technologies, and Open Research Problemscited by 160
  3. 2019Quantum Natural Gradientcited by 106
  4. 2025Hierarchical Deep Reinforcement Learning for Adaptive Resource Management in Integrated Terrestrial and Non-Terrestrial Networkscited by 2

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