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.
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.
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.
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.
This paper compares the performance of a quantum convolutional neural network (QCNN) and a conventional CNN for signal detection in a RIS-assisted single-user downlink (SU-MISO) system, covering both passive and active RIS models. Assuming perfect channel state information (CSI), the RIS phases are optimized using an SVD-based method. Monte Carlo simulations show that the QCNN outperforms the CNN while using far fewer trainable parameters, resulting in a substantially smaller model. The study focuses on parameter efficiency alongside performance gains.
Recent advances in quantum computing are opening new possibilities for Earth Observation (EO) data analysis. Quantum machine learning (QML) approaches offer novel ways to process information by exploiting quantum phenomena such as superposition and entanglement. These capabilities have motivated the exploration of whether quantum-enhanced models can address long-standing challenges in satellite remote sensing, where complex spectral and spatial signals often require sophisticated feature extraction. Among various fields of application, EO data allow the global monitoring of volcanic clouds and are crucial for aviation safety, hazard assessment, real-time eruption response, and evaluation of volcanic impacts on climate. Yet accurate detection of volcanic clouds remains difficult due to their similarity with meteorological clouds, the variability of eruption signatures, and the coarse spectral sampling of geostationary sensors. In this work, the potential of hybrid quantum convolutional neural networks (QCNNs) for the classification of satellite images containing volcanic clouds was investigated. These architectures integrate quantum computational layers into a classical convolutional framework. Two QCNN variants (with 2 and 4 qubits) have been considered to evaluate their ability to classify a dataset of SEVIRI images, including scenes with volcanic clouds (composed of ash, SO2, or mixed components) as well as non-volcanic backgrounds. Finally, the performance of the hybrid QCNN models was compared with that of purely classical architectures.
This study evaluates the use of Quantum Convolutional Neural Networks (QCNNs) for identifying signals resembling Gamma-Ray Bursts (GRBs) within simulated astrophysical datasets in the form of light curves. The task addressed here focuses on distinguishing GRB-like signals from background noise in simulated Cherenkov Telescope Array Observatory (CTAO) data, the next-generation astrophysical observatory for very high-energy gamma-ray science. QCNNs, a quantum counterpart of classical Convolutional Neural Networks (CNNs), leverage quantum principles to process and analyze high-dimensional data efficiently. We implemented a hybrid quantum-classical machine learning technique using the Qiskit framework, with the QCNNs trained on a quantum simulator. Several QCNN architectures were tested, employing different encoding methods such as Data Reuploading and Amplitude encoding. Key findings include that QCNNs achieved accuracy comparable to classical CNNs, often surpassing 90%, while using fewer parameters, potentially leading to more efficient models in terms of computational resources. A benchmark study further examined how hyperparameters like the number of qubits and encoding methods affected performance, with more qubits and advanced encoding methods generally enhancing accuracy but increasing complexity. QCNNs showed robust performance on time-series datasets, successfully detecting GRB signals with high precision. The research is a pioneering effort in applying QCNNs to astrophysics, offering insights into their potential and limitations. This work sets the stage for future investigations to fully realize the advantages of QCNNs in astrophysical data analysis.
Furthermore, the physics and multimodality imaging characteristics of tumors, along with high variability in intensity, morphology and anatomical position of the tumor, make the diagnosis of tumors from magnetic resonance imaging (MRI) still quite difficult. In this study, we introduce a hybrid quantum-classical Quantum Convolutional Neural Network (QCNN) that applies Quantum processing in the localized stage of feature extraction rather than later in the classification stage when most current quantum neural networks (CNNs) are used, for automated tumor detection from multiparametric MRI data. The proposed framework makes use of the BraTS 2021 data set, comprising of T1-weighted, post-contrast T1-weighted (T1Gd), T2-weighted and T2-FLAIR images, along with patient-level partitioning, intensity normalization, tumor relevant slice selection, 4×4/8×8 patch generation, dimensionality reduction, quantum angle encoding, trainable variational quantum transformations and Pauli-Z expectation-value measurements. This produced quantum feature maps which were summed across classical layers, and compared to a standard CNN and transfer learning CNN and CNN with a quantum classifier. The proposed QCNN showed excellent performance with 96% accuracy, 93% sensitivity, 94% specificity, 94% precision, 94% F1-score, AUPRC of 0.94, AUROC of 0.96 and the MCC score is 0.82, which is the best among all the architectures considered. The use of architectural analysis revealed to be an option, since 4-6 qubits, 2-3 quantum layers, angle encoding and linear or ring entanglement prove to be adequate. The results of robustness analysis showed that the performance is degrades progressively as the level of quantum noise is increased but still reached a good AUROC (0.80) in the case of strong quantum noise, and was confirmed by an independent external test with a value of 0.85. The results here show trainable quantum convolution to be one potential approach to localized multimodal representation learning for MRI, and present an indication of the impact of quantum noise, finite shot execution, and dataset shift. The study provides a groundwork for the further expansion of research into the use of quantum-enhanced MRI for analysis, in the form of an experimentally controlled experiments design, in larger independent cohorts of patients and with real quantum infrastructure.