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.
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.
Intelligent reflecting surfaces (IRSs) have attracted attention as a technology that can considerably improve the energy utilization efficiency of sixth-generation (6G) mobile communication systems. IRSs enable control of propagation characteristics by adjusting the phase shift of each reflective element. However, designing the phase shift requires the acquisition of channel information for each reflective element, which is impractical from an overhead perspective. In addition, for multiple wireless network operators to share an IRS for communication, new infrastructure facilities and operational costs are required at each operator's end to control the IRS in a coordinated manner. Herein, we propose a wireless communication system using standalone IRSs to solve these problems. The standalone IRSs cover a wide area by periodically switching phase shifts, and each operator allocates radio resources according to their phase-shift switching. Furthermore, we derive a quadratic unconstrained binary optimization equation for the proposed system to optimize radio resource allocation using quantum computing. The results of computer simulations indicate that the proposed system and method can be used to achieve efficient communication in 6G mobile communication systems.
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.
Image classification plays a vital role in large-scale data analysis, especially in object recognition tasks using advanced Deep Learning (DL) frameworks. However, the growing complexity and computational demands of modern DL models have introduced challenges related to scalability and efficiency. Quantum Computing (QC) has emerged as a promising alternative, capable of addressing these limitations by leveraging the principles of Quantum Machine Learning (QML). However, many existing QML models require a large number of qubits, which poses limitations within the current Noisy Intermediate-Scale Quantum (NISQ) era. This work introduces a novel Adaptive Quantum Convolutional Neural Network (AQCNN) designed for efficient and scalable image classification within the constraints of NISQ devices. Addressing the limitations of existing QML approaches, particularly the high qubit requirements, AQCNN incorporates a resource-efficient quantum convolutional layer that performs localized quantum filtering using parameterized quantum circuits. A classical preprocessing layer encodes input features to reduce qubit load, followed by quantum embedding and hybrid quantum-classical layers that optimize feature extraction and classification performance. The model leverages an adaptive quantum convolution strategy, minimizing quantum gate depth and circuit complexity while preserving critical spatial hierarchies in image data. Evaluated on benchmark datasets, AQCNN achieved 95.88% accuracy on MNIST and 95.68% on FMNIST, outperforming comparable QML architectures. Additionally, the model supports scalable execution through parallel quantum circuit arrays, enabling practical deployment on current quantum hardware. This architecture demonstrates a significant advance in quantum-assisted image classification, balancing performance with qubit and gate efficiency. The integration of adaptive quantum convolution and hybrid processing not only enhances classification accuracy but also provides a viable path forward for deploying QML solutions under realistic hardware constraints.
Convolutional Neural Networks (CNNs) are extensively utilized for image classification due to their ability to exploit data correlations effectively. However, traditional CNNs encounter several challenges, including limitations in accuracy improvement, computational efficiency, explain ability, and generalization. As data dimensionality or model complexity increases, CNNs often become more difficult to train efficiently, leading to slower processing times. To overcome these challenges, Quantum Convolutional Neural Networks (QCNNs) offer a promising quantum-based approach to enhance existing models, particularly by improving training efficiency and reducing processing times. This paper provides a comparative analysis of classical CNNs and a novel quantum circuit architecture tailored for image-based tasks. The analysis emphasizes the adaptability and potential of quantum circuits to enhance feature extraction, recognition accuracy, and processing efficiency.We applied various QCNN architectures to the Chinese-MNIST dataset and compared their performance with traditional CNN models. Although QCNNs achieved slightly lower accuracy, they demonstrated significantly greater stability. These findings high- light QCNNs’ potential to overcome the computational limitations of conventional deep learning, offering a promising pathway for quantum-enhanced solutions in computer vision. Future work will focus on refining quantum circuit designs, developing hybrid quantum-classical models, and extending QCNN applications to larger and more diverse datasets.
The integration of quantum computing principles with classical deep learning architectures has emerged as a promising direction for advancing image recognition systems beyond the limitations of purely classical convolutional neural networks (CNNs). This study proposes and analyzes a hybrid quantum–classical deep learning framework based on quanvolutional neural network (QNN) principles, where quantum circuits are embedded within convolutional feature extraction layers to enhance representation capability. The research investigates how quantum-enhanced filters, particularly quanvolutional layers, contribute to improved feature diversity, non-linear transformation capacity, and classification accuracy in image recognition tasks. Building upon established convolutional architectures and quantum machine learning theories, the proposed framework integrates quantum random circuit encoding with classical deep neural networks to evaluate performance gains in comparison to conventional CNN models. The study further incorporates theoretical insights from hybrid quantum-classical learning systems, emphasizing the role of parameterized quantum circuits in feature mapping and optimization stability. The methodology is grounded in NISQ-era quantum computing constraints and leverages hybrid optimization strategies to ensure computational feasibility. Experimental evaluation demonstrates that quanvolution-based models can provide improved robustness in feature extraction, particularly in high-dimensional image spaces, while maintaining competitive computational efficiency. However, scalability limitations and noise sensitivity in quantum circuits remain key challenges. Overall, this work contributes to the growing field of quantum-enhanced machine learning by providing a structured performance analysis of hybrid quanvolutional architectures and highlighting their potential role in next-generation intelligent visual recognition systems.
This study explores various architectures of Quantum Neural Networks (QNN) that utilize quantum computing to enhance machine learning model performance. The architectures examined include Hybrid Quantum Neural Network (H-QNN), Deep Quantum Neural Network (DQNN), Quantum Convolutional Neural Network (QCNN), and Coherent Feed Forward Quantum Neural Network. Additionally, the EfficientSU2 architecture demonstrated superior performance in accuracy and cost reduction. Optimization techniques such as Adaptive Moment Estimation (ADAM), Analytic Quantum Gradient Descent (AQGD), and Nakanishi-Fujii-Todo (NFT) were integrated to improve training efficiency. The study results show that EfficientSU2 with 10,000 iterations yielded the best results in terms of cost and accuracy across all tested datasets, with accuracy improvements reaching up to 97%. In conclusion, the EfficientSU2 architecture and the applied optimization techniques significantly enhanced the processing of complex data, paving the way for substantial advancements in quantum-based artificial intelligence applications.