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

TL;DR

The paper introduces QPPG, a quantum-inspired reinforcement learning algorithm that stabilizes policy gradients, leading to faster convergence and improved throughput and power efficiency in wireless link adaptation.

Contribution

It presents the quantum-preconditioned policy gradient (QPPG) algorithm, applying Fisher-information-based preconditioning to enhance RL stability and performance in fading channels.

Findings

QPPG converges faster than classical methods.

Achieves 28.6% higher throughput.

Reduces transmit power by 43.8%.

Abstract

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.