Hybrid quantum-classical neural networks for low earth orbit satellite communications in 6G

Low Earth Orbit (LEO) satellite communications are pivotal for next-generation 6G wireless systems but suffer from severe channel impairments, including large Doppler shifts and time-varying Shadowed-Rician fading. These dynamic conditions, coupled with channel aging, significantly degrade the reliability of classical receivers. While deep learning-based solutions offer improved performance, their high computational complexity poses a challenge for resource-constrained satellite payloads. This paper proposes a parameter-efficient hybrid quantum-classical receiver based on a Quantum Convolutional Neural Network. In the remainder of this paper, the receiver is referred to as the Hybrid QCNN. The model is enhanced with an iterative data re-uploading mechanism to maximize the expressivity of limited qubits. To improve optimization stability in hybrid quantum-classical training, this paper introduces a Dual Learning Rate optimization strategy. In this setting, gradient magnitudes can be small and can differ across parameter groups, so the strategy assigns distinct learning rates to the quantum and classical parameters. This paper validates the proposed model using a high-fidelity channel simulator that integrates the 3GPP TR 38.811 elevation-dependent K-factor model with Loo's Shadowed-Rician fading model, dynamically capturing realistic fading characteristics across different elevation angles. Simulation results demonstrate that the proposed Hybrid QCNN achieves bit error rate performance that is nearly identical to that of the classical CNN baseline. The performance gap remains below 1% across varying channel conditions, while the number of parameters is reduced by approximately 89%. Furthermore, in an ablation study, the iterative re-processing mechanism achieves up to a 26.9% relative BER reduction at an SNR of 10 dB compared with a standard quantum pooling baseline on the considered test set. This result indicates improved feature preservation during quantum pooling.