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.
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.
This paper investigates advanced signal processing techniques to address the critical challenge of Multi-User Interference (MUI) within the demanding framework of 6th Generation (6G) wireless systems. The study presents a direct comparative analysis between classical deep learning and emerging quantum machine learning paradigms. We first establish the capabilities of Convolutional Neural Networks (CNNs), a state-of-the-art deep learning model, in handling the complex, non-ideal channel environments of modern wireless communications. Subsequently, we explore the theoretical advantages and practical performance of Quantum Convolutional Neural Networks (QCNNs), positing them as a next-generation solution for processing unprecedented data complexity. Through rigorous simulation in a multi-user SIMO uplink scenario, this paper demonstrates that QCNN consistently outperforms its classical CNN counterpart in both signal detection accuracy and overall system sum rate, particularly in low signal-to-noise ratio (SNR) regimes, all while utilizing significantly fewer model parameters. The core contribution of this work is to provide direct empirical evidence of the potential quantum advantage offered by QCNNs for the complex signal processing tasks inherent to future 6G systems.
A reconfigurable intelligent surface (RIS) is a promising technology for beyond-fifth-generation (B5G) and sixth-generation (6G) wireless communications, but its passive reflection and two-hop double-fading structure make cascaded channel estimation challenging. Conventional convolutional neural network (CNN) estimators require many trainable parameters, while a single shallow parameterized quantum circuit (PQC) may have limited feature representation. Deep quantum circuits can also suffer from noise and barren-plateau effects on noisy intermediate-scale quantum (NISQ) devices. To address these issues, this paper proposes a multi-filter quantum convolutional neural network (MF-QCNN) for cascaded channel estimation in RIS-assisted multi-user uplink systems. The proposed model uses multiple independent shallow PQC filters in parallel, concatenates their measured features, and estimates the cascaded channel through a compact classical dense head, with the total trainable-parameter count scaling as 182F+696 for F parallel filters. Simulation results, compared with a single-filter quantum convolutional neural network (QCNN), CNN, and multilayer perceptron (MLP) baselines, show that at a signal-to-noise ratio (SNR) of 20 dB, the 3-filter MF-QCNN reduces the normalized mean squared error (NMSE) by approximately 22.9, 8.1, and 4.6 dB relative to the single-filter QCNN, CNN, and MLP baselines, respectively, while using only about 19.3% of the CNN trainable parameters. Under zero-forcing (ZF) precoding, it achieves the highest achievable sum rate among the learning-based estimators; at SNR = 30 dB, it improves the achievable sum rate by approximately 17.4% and 12.8% over the CNN and MLP baselines, respectively. These simulation results suggest that the parallel shallow-PQC design can serve as a compact quantum-aided estimator for RIS channel estimation and may provide a useful basis for future studies on AI-native transceiver design in B5G/6G networks.
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.
This paper presents a comprehensive evaluation of the potential of Quantum Convolutional Neural Networks (QCNNs) in comparison to classical Convolutional Neural Networks (CNNs) and Artificial / Classical Neural Network (ANN) models. With the increasing amount of data, utilizing computing methods like CNN in real-time has become challenging. QCNNs overcome this challenge by utilizing qubits to represent data in a quantum environment and applying CNN structures to quantum computers. The time and accuracy of QCNNs are compared with classical CNNs and ANN models under different conditions such as batch size and input size. The maximum complexity level that QCNNs can handle in terms of these parameters is also investigated. The analysis shows that QCNNs have the potential to outperform both classical CNNs and ANN models in terms of accuracy and efficiency for certain applications, demonstrating their promise as a powerful tool in the field of machine learning.
The rapid increase in data consumption by mobile users has created a demand for innovative architectures to address this growing need. Concurrently, classical neural network-based intelligent reflecting surfaces (IRS) have gained traction in 6G networks due to their capacity to enhance spectral efficiency (SE). However, relying solely on classical computing for SE calculations imposes limitations on computational rates, stemming from the exponential increase in complexity. In response to this challenge, this paper proposes the use of quantum convolutional neural networks (QCNNs) to improve the SE of communication systems facilitated by IRS configurations. To optimize IRS phase shifts, we first apply singular value decomposition to the received IRS-optimized data. The resulting information is then incorporated into the quantum data and subsequently processed by the parameterized quantum circuit. A dense layer is introduced after the proposed model to boost training performance. The proposed QCNN model demonstrates a significant enhancement in SE while maintaining lower complexity, leveraging the capabilities of qubits in the current era of quantum computing.
Wireless Sensor Network (WSN)-assisted Internet of Things (IoT) systems have revolutionized modern healthcare by enabling real-time medical image acquisition, transmission, and remote diagnostics. However, these systems face critical challenges such as limited energy resources, bandwidth constraints, and degradation of image quality during transmission and processing. Ensuring energy-efficient computation while preserving diagnostic image quality is therefore essential for reliable medical decision-making. Artificial Intelligence (AI)-driven approaches, particularly Quantum Convolutional Neural Networks (QCNNs) integrated with attention-based models, have emerged as promising solutions to address these challenges. Quantum Convolutional Neural Networks leverage principles of quantum computing such as superposition and entanglement to process high-dimensional data more efficiently than classical convolutional neural networks. QCNN architectures can significantly reduce model parameters and computational complexity while maintaining high accuracy, making them suitable for resource-constrained WSN environments. Studies indicate that QCNNs can outperform classical CNNs in terms of efficiency and scalability, especially in complex image classification tasks. Additionally, hybrid quantum-classical models further enhance performance by combining classical feature extraction with quantum optimization capabilities. Attention-based models play a vital role in improving medical image diagnostics by focusing on relevant regions of interest and suppressing noise. Deep learning techniques, particularly CNN-based models, have demonstrated superior performance in medical image understanding tasks such as classification, segmentation, and detection. Attention mechanisms enhance these capabilities by improving feature representation and preserving critical diagnostic information, thereby improving overall image quality.