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.
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.
Signal detection in Geostationary Earth Orbit (GEO) satellite links can be degraded by Rician fading, receiver noise and imperfect channel state information (CSI). This paper investigates a parameter-count-efficient hybrid quantum-classical detector for GEO satellite Single-Input Single-Output (SISO) BPSK links. The objective is to evaluate whether a compact Quantum Convolutional Neural Network (QCNN) feature-processing block can provide CNN-comparable uncoded bit-detection reliability under a controlled imperfect-CSI software-simulation setup, while keeping the trainable model size small. The channel and SNR normalizations are explicitly stated, and the conventional random Line-of-Sight (LOS) phase in the Rician model is addressed through a phase-invariance argument for the proposed channel-compensated features. Four physics-based diagnostic features are extracted from a channel-compensated observation and encoded into a 4-qubit QCNN. The QCNN contains 2,289 trainable parameters, including 48 quantum-circuit parameters and 2,241 classical post-processing parameters, whereas the same-input CNN baseline contains 12,609 trainable parameters. Simulations from − 10 dB to 20 dB show that the QCNN achieves 89.39% average accuracy, compared with 89.74% for the CNN. As a complementary reliability-oriented metric, a capacity-weighted effective-throughput indicator is also evaluated, where the QCNN achieves an average effective throughput of 2.376 bps/Hz, compared with 2.406 bps/Hz for the CNN. These results suggest that the proposed QCNN can provide a favorable balance between detection reliability, effective-throughput behavior and trainable parameter count. This balance is particularly relevant to isolated GEO satellite receiver environments, where onboard memory, model-update opportunities and processing resources are limited after deployment.
Satellite communications (SatCom) are envisioned as a critical enabler of 6G networks, enabling seamless global coverage by integrating terrestrial infrastructures with multi-layered satellite constellations. Among these, the integration between geostationary (GEO) and low Earth orbit (LEO) satellite networks is particularly attractive, as they combine the broad coverage of GEO satellites with the low latency and high capacity of LEO systems. Within this context, we address the resource allocation problem for LEO satellite through a joint design of beam size and transmit power, while accounting for GEO interference constraints, residual Doppler frequency offsets, and frequency reuse strategies. The objective is to maximize the spectral efficiency of LEO system operating in multi-beam GEO-LEO networks. Motivated by the limitations of classical deep reinforcement learning (RL) in such dynamic orbital settings and the potential of quantum RL for accelerated convergence, we propose a hybrid solution that exploits quantum acceleration during offline training and subsequently exports the learned policy into a classical representational format for onboard LEO satellite deployment. A fully quantum deep deterministic policy gradient framework with variational quantum circuit-based actor and critic is developed, along with a neural network-based policy translator for classical inference. To the best of our knowledge, this is the first deployment-ready quantum RL framework in SatCom, offering efficient offline training, reduced retraining latency, and practical deployment compatibility with existing LEO satellite hardware.
Accurate channel estimation is a fundamental requirement for high-performance wireless communication systems, where conventional deep learning approaches often suffer from excessive trainable parameters and high memory cost as antenna and subcarrier dimensions scale up. This paper proposes a quantum machine learning (QML)-based channel estimation framework that addresses this scalability challenge through a compact hybrid quantum-classical architecture. An 8-qubit quantum neural network (QNN) based on a parameterized quantum circuit (PQC) encodes received pilot-domain features using both Cartesian and polar components through Hadamard initialization and rotation-based quantum encoding, and learns channel representations using trainable variational rotations and controlled-Z (CZ) entanglement. The Pauli-Z measurement outputs are then processed by a lightweight classical dense head to produce the final channel estimate. The proposed QNN is compared with a classical deep neural network (DNN) and a QNN without hidden dense layers over a 2 × 1 Rayleigh fading multiple-input single-output (MISO) channel across an SNR range of 10 dB to 30 dB. Simulation results show that the proposed hybrid QNN achieves the lowest normalized mean square error (NMSE) across the entire evaluated SNR range while using only 1,012 trainable parameters, compared with 3,356 for the DNN. At 30 dB SNR, the proposed QNN achieves the lowest NMSE of approximately − 20.24 dB, compared with approximately − 17.54 dB for the DNN baseline. An ablation study further confirms that the dense post-processing head is essential for accurate regression from raw quantum measurement outputs. These results suggest that compact hybrid quantum-classical estimators offer a promising direction for accurate and parameter-efficient channel estimation in next-generation wireless communication systems.
Deep learning has achieved significant success in pattern recognition, with convolutional neural networks (CNNs) serving as a foundational architecture for extracting spatial features from images. Quantum computing provides an alternative computational framework, a hybrid quantum-classical convolutional neural networks (QCCNNs) leverage high-dimensional Hilbert spaces and entanglement to surpass classical CNNs in image classification accuracy under comparable architectures. Despite performance improvements, QCCNNs typically use fixed quantum layers without incorporating trainable quantum parameters. This limits their ability to capture non-linear quantum representations and separates the model from the potential advantages of expressive quantum learning. In this work, we present a hybrid quantum-classical-quantum convolutional neural network (QCQ-CNN) that incorporates a quantum convolutional filter, a shallow classical CNN, and a trainable variational quantum classifier. This architecture aims to enhance the expressivity of decision boundaries in image classification tasks by introducing tunable quantum parameters into the end-to-end learning process. Through a series of small-sample experiments on MNIST, F-MNIST, and MRI tumor datasets, QCQ-CNN demonstrates competitive accuracy and convergence behavior compared to classical and hybrid baselines. We further analyze the effect of ansatz depth and find that moderate-depth quantum circuits can improve learning stability without introducing excessive complexity. Additionally, simulations incorporating depolarizing noise and finite sampling shots suggest that QCQ-CNN maintains a certain degree of robustness under realistic quantum noise conditions. While our results are currently limited to simulations with small-scale quantum circuits, the proposed approach offers a potentially promising direction for hybrid quantum learning in near-term applications.
Medical image classification poses significant challenges due to the complex and nonlinear patterns present in imaging data. Traditional deep learning models often struggle to capture the intricate relationships inherent in such data, especially under conditions of noise and training instability. To address these limitations, this study proposes a novel Hybrid Quantum Convolutional Neural Network (HQCNN) integrated with a Gradient Variance-Controlled Optimizer (GVCO). The aim is to enhance diagnostic accuracy by combining classical convolutional layers for shallow feature extraction with a parameterized quantum circuit (PQC) that enables richer and more discriminative feature representations. The GVCO dynamically adjusts learning rates and momentum coefficients based on real-time gradient variance, improving convergence speed and generalization performance. The proposed HQCNN-GVCO model was evaluated on two public brain imaging datasets: the Kaggle Brain MRI dataset and the REMBRANDT dataset. It achieved classification accuracies of 99.2% and 98.2%, respectively. Compared to baseline HQCNN and other state-of-the-art models, HQCNN-GVCO demonstrated superior and more consistent performance. These findings highlight the potential of integrating quantum computing techniques with adaptive optimization strategies to advance reliable and accurate medical image analysis.
The objective of this research is to develop a Hybrid Quantum-Classical Convolutional Neural Network (QC-CNN) framework that integrates parameterized quantum circuits (PQCs) within classical CNN architectures to enhance image classification performance.The proposed model leverages quantum principles such as superposition and entanglement for high-dimensional feature representation, achieving superior accuracy and reduced computational complexity.Experiments on MNIST and CIFAR-10 datasets demonstrated improved classification accuracy of 98.7% and 82.5%, respectively, surpassing traditional CNNs while reducing training time by 33% and parameters by 45%.Statistical analysis confirmed the significance of these improvements.Visualizations using t-SNE revealed enhanced class separability, and noise perturbation tests validated the model's robustness.The results highlight the hybrid QC-CNN's potential for efficient and scalable quantum-enhanced deep learning applications.Extended evaluations, including quantum-layer-depth analysis, robustness testing, and t-SNE visualization, empirically support the hybrid QC-CNN's effectiveness across varied scenarios.