The emergence of Beyond 5G (B5G) and 6G networks introduces advanced capabilities in speed, latency, and connectivity, while also amplifying the risk of sophisticated security threats such as False Base Stations (FBS). These rogue entities imitate legitimate base stations to intercept data, perform man-in-the-middle attacks, or disrupt services. Researchers have explored conventional machine learning methods for FBS detection, but these methods often struggle to address the high-dimensional and dynamic nature of next-generation networks. This paper proposes a Quanvolutional Neural Network (QNN) framework as a quantum-enhanced solution for FBS detection. QNN utilizes quantum phenomena such as superposition and entanglement to perform complex feature transformations, enabling more expressive data representations than classical models. We evaluate the proposed QNN against a traditional Convolutional Neural Network (CNN) using a classification task designed to simulate the detection of FBS. Results demonstrate that although the QNN exhibits slower convergence during early training, it ultimately outperforms the CNN across all key evaluation metrics. The QNN achieves higher accuracy (90.65%), improved recall, lower false positive rate, and a superior F1 score, while also attaining a lower final training loss. These findings suggest that QNNs have strong potential for real-time, localized threat detection in AInative 6G architectures. Future work will explore deployment on Noisy Intermediate-Scale Quantum (NISQ) hardware and evaluate model performance under realistic noise conditions. This study contributes to ongoing research on integrating quantum machine learning into secure and intelligent wireless communication systems
The emergence of Beyond 5G (B5G) and 6G networks introduces advanced capabilities in speed, latency, and connectivity, while also amplifying the risk of sophisticated security threats such as False Base Stations (FBS). These rogue entities imitate legitimate base stations to intercept data, perform man-in-the-middle attacks, or disrupt services. Researchers have explored conventional machine learning methods for FBS detection, but these methods often struggle to address the high-dimensional and dynamic nature of next-generation networks. This paper proposes a Quanvolutional Neural Network (QNN) framework as a quantum-enhanced solution for FBS detection. QNN utilizes quantum phenomena such as superposition and entanglement to perform complex feature transformations, enabling more expressive data representations than classical models. We evaluate the proposed QNN against a traditional Convolutional Neural Network (CNN) using a classification task designed to simulate the detection of FBS. Results demonstrate that although the QNN exhibits slower convergence during early training, it ultimately outperforms the CNN across all key evaluation metrics. The QNN achieves higher accuracy (90.65%), improved recall, lower false positive rate, and a superior F1 score, while also attaining a lower final training loss. These findings suggest that QNNs have strong potential for real-time, localized threat detection in AInative 6G architectures. Future work will explore deployment on Noisy Intermediate-Scale Quantum (NISQ) hardware and evaluate model performance under realistic noise conditions. This study contributes to ongoing research on integrating quantum machine learning into secure and intelligent wireless communication systems
The growing complexity and velocity of cyber threats in high-security environments such as defense, critical infrastructure, and intelligence networks necessitates a paradigm shift in threat detection capabilities. Traditional cybersecurity systems, including those enhanced by classical machine learning algorithms, often struggle to process and classify massive volumes of heterogeneous and encrypted data in real time. This shortcoming is particularly evident in the context of advanced persistent threats (APTs), polymorphic malware, and insider attacks, which require rapid adaptation and heightened sensitivity to anomalous behavior.Quantum Machine Learning (QML), an emerging interdisciplinary field at the intersection of quantum computing and artificial intelligence, presents a promising avenue for augmenting threat detection mechanisms. Leveraging quantum phenomena such as superposition and entanglement, QML models offer potential advantages in processing speed, pattern recognition, and feature space transformation that can outperform their classical counterparts in high-dimensional data analysis. This paper explores the application of QML to threat detection in high-security networks, proposing a hybrid quantum-classical framework that integrates quantum-enhanced classifiers such as quantum support vector machines and variational quantum circuits into existing detection pipelines.The study outlines a technical overview of quantum computing principles relevant to cybersecurity, critically evaluates existing detection architectures, and presents simulation-based case studies to assess performance metrics, including detection accuracy and false positive rates. It further examines the limitations of current quantum hardware, algorithmic constraints, and emerging ethical and operational considerations. The findings suggest that while QML is still constrained by hardware maturity and integration complexity, it holds transformative potential for proactive, intelligent, and adaptive cyber defense systems in high-stakes environments. This research contributes to ongoing efforts to future-proof cybersecurity infrastructure against both classical and post-quantum threat landscapes.
The upcoming fifth generation (5G) of wireless networks is expected to lay a foundation of intelligent networks with the provision of some isolated artificial intelligence (AI) operations. However, fully intelligent network orchestration and management for providing innovative services will only be realized in Beyond 5G (B5G) networks. To this end, we envisage that the sixth generation (6G) of wireless networks will be driven by on-demand self-reconfiguration to ensure a many-fold increase in the network performance and service types. The increasingly stringent performance requirements of emerging networks may finally trigger the deployment of some interesting new technologies, such as large intelligent surfaces, electromagnetic-orbital angular momentum, visible light communications, and cell-free communications, to name a few. Our vision for 6G is a massively connected complex network capable of rapidly responding to the users' service calls through real-time learning of the network state as described by the network edge (e.g., base-station locations and cache contents), air interface (e.g., radio spectrum and propagation channel), and the user-side (e.g., battery-life and locations). The multi-state, multi-dimensional nature of the network state, requiring the real-time knowledge, can be viewed as a quantum uncertainty problem. In this regard, the emerging paradigms of machine learning (ML), quantum computing (QC), and quantum ML (QML) and their synergies with communication networks can be considered as core 6G enablers. Considering these potentials, starting with the 5G target services and enabling technologies, we provide a comprehensive review of the related state of the art in the domains of ML (including deep learning), QC, and QML and identify their potential benefits, issues, and use cases for their applications in the B5G networks. Subsequently, we propose a novel QC-assisted and QML-based framework for 6G communication networks while articulating its challenges and potential enabling technologies at the network infrastructure, network edge, air interface, and user end. Finally, some promising future research directions for the quantum- and QML-assisted B5G networks are identified and discussed.
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.
The rise of deepfake technologies has posed significant challenges to privacy, security, and information integrity, particularly in audio and multimedia content. This paper introduces a Quantum-Trained Convolutional Neural Network (QT-CNN) framework designed to enhance the detection of deepfake audio, leveraging the computational power of quantum machine learning (QML). The QT-CNN employs a hybrid quantum-classical approach, integrating Quantum Neural Networks (QNNs) with classical neural architectures to optimize training efficiency while reducing the number of trainable parameters. Our method incorporates a novel quantum-to-classical parameter mapping that effectively utilizes quantum states to enhance the expressive power of the model, achieving up to 70% parameter reduction compared to classical models without compromising accuracy. Data pre-processing involved extracting essential audio features, label encoding, feature scaling, and constructing sequential datasets for robust model evaluation. Experimental results demonstrate that the QT-CNN achieves comparable performance to traditional CNNs, maintaining high accuracy during training and testing phases across varying configurations of QNN blocks. The QT framework’s ability to reduce computational overhead while maintaining performance underscores its potential for real-world applications in deepfake detection and other resource-constrained scenarios. This work highlights the practical benefits of integrating quantum computing into artificial intelligence, offering a scalable and efficient approach to advancing deepfake detection technologies.
The escalating threat and impact of network-based attacks necessitate innovative intrusion detection systems. Machine learning has shown promise, with recent strides in Quantum Machine Learning (QML) offering new avenues. However, the potential of quantum computing is tempered by challenges in current noisy intermediate-scale quantum (NISQ) era machines. In this study, we explore Quantum Neural Networks (QNNs) for intrusion detection, optimizing their performance within current quantum computing limitations. Our approach includes efficient classical feature encoding, QNN classifier selection, and performance tuning leveraging current quantum computational power. This study culminates in an optimized multi-layered QNN architecture for network intrusion detection. A small version of the proposed architecture was implemented on IonQ's Aria-1 quantum computer, achieving a notable 0.86 F1 score using the NF-UNSW-NB15 dataset. Additionally, we introduce a novel metric, certainty factor, laying the foundation for future integration of uncertainty measures in quantum classification outputs. Moreover, this factor is used to predict the noise susceptibility of our quantum binary classification system.
The rapid expansion of the Internet of Things in critical industrial environments has significantly increased the attack surface, exposing systems to sophisticated cyber threats. Traditional pattern-based intrusion detection systems struggle to detect such advanced attacks, while deep learning approaches, despite achieving high detection accuracy, often suffer from high computational cost and latency, limiting their deployment in resource-constrained edge gateways. Quantum machine learning offers a promising alternative by enabling high-dimensional feature representation; however, current implementations are constrained by hardware noise, limited qubit availability and backend-dependent execution characteristics in the Noisy Intermediate-Scale Quantum era. To address these challenges, this paper proposes a Residual Hybrid Quantum-Classical Neural Network (RHQ-CNN) for efficient intrusion detection in Edge-Industrial Internet of Things (Edge-IIoT) environments under idealized quantum simulation conditions. The proposed framework employs a classical neural network encoder to compress high-dimensional network traffic into a compact latent representation suitable for quantum processing. A variational quantum circuit with serial data re-uploading is then utilised to model complex non-linear decision boundaries without increasing qubit requirements. In addition, a residual connection fuses classical and quantum representations to improve training stability and preserve latent feature information. The model is evaluated on the Edge-IIoTset dataset and achieves a test accuracy of 99.94%, with high weighted performance across the 15-class detection task, although the extremely low-sample Fingerprinting class remains comparatively more challenging. Additional controlled ablation, deployment-cost, and bootstrap confidence interval analyses demonstrate the test-set metric stability and computational trade-offs of the proposed architecture. These findings highlight the potential of hybrid quantum-classical models for next-generation cybersecurity in industrial IoT systems.