Abstract This paper presents a novel hybrid architecture that integrates Software Defined Radio (SDR) with Quantum Machine Learning (QML) for secure, adaptive, and high-performance wireless communication. The proposed system utilizes Offset Quadrature Amplitude Modulation (OQAM) for enhanced spectral efficiency and reduced inter-symbol interference, benefiting from the nonlinear signal properties managed by filter bank-based multicarrier systems. Quantum Support Vector Machines (QSVM), Quantum Reservoir Computing (QRC), and Quantum Neural Networks (QNN) are deployed to classify modulation types and manage spectrum allocation in real-time. Simulation results derived from GNU Radio and the PennyLane framework indicate that the suggested system attains a modulation classification accuracy of up to 96.3%. The QNN model trained on OQAM signals achieves an accuracy of 94.7% and an end-to-end processing latency of around 16.2 milliseconds. The design reliably sustains classification performance over 90% throughout a signal-to-noise ratio (SNR) range of 0 to 20 dB, even in the presence of noise. Secure transmission is guaranteed by a conventional control interface that enables Quantum Key Distribution (QKD), included inside the SDR environment. The integration of reconfigurable SDR hardware with nonlinear quantum machine learning algorithms positions this method as a formidable option for dynamic spectrum optimization and secure communications in forthcoming 5G and 6G networks.
Abstract This paper presents a novel hybrid architecture that integrates Software Defined Radio (SDR) with Quantum Machine Learning (QML) for secure, adaptive, and high-performance wireless communication. The proposed system utilizes Offset Quadrature Amplitude Modulation (OQAM) for enhanced spectral efficiency and reduced inter-symbol interference, benefiting from the nonlinear signal properties managed by filter bank-based multicarrier systems. Quantum Support Vector Machines (QSVM), Quantum Reservoir Computing (QRC), and Quantum Neural Networks (QNN) are deployed to classify modulation types and manage spectrum allocation in real-time. Simulation results derived from GNU Radio and the PennyLane framework indicate that the suggested system attains a modulation classification accuracy of up to 96.3%. The QNN model trained on OQAM signals achieves an accuracy of 94.7% and an end-to-end processing latency of around 16.2 milliseconds. The design reliably sustains classification performance over 90% throughout a signal-to-noise ratio (SNR) range of 0 to 20 dB, even in the presence of noise. Secure transmission is guaranteed by a conventional control interface that enables Quantum Key Distribution (QKD), included inside the SDR environment. The integration of reconfigurable SDR hardware with nonlinear quantum machine learning algorithms positions this method as a formidable option for dynamic spectrum optimization and secure communications in forthcoming 5G and 6G networks.
Quantum communication has emerged as a transformative technology for secure information transfer, overcoming noise, signal loss, and limited data throughput. Traditional adaptive modulation techniques address the uncertainties of quantum channels. This paper introduces quantum machine learning (QML)-based adaptive modulation models to enhance the performance of quantum communication systems by optimising fidelity, throughput, and error resilience. The modulation selection problem is framed as a multi-objective utility optimisation, balancing key metrics such as fidelity, throughput, and bit error rate (BER). We employ variational quantum neural networks (VQNNs) trained on real-world parameters from fibre optics and satellite-based links, depolarising noise, and amplitude-damping effects. Experimental results indicate that fidelity decreases significantly with distance and noise, with a drop from 1.0 to 0.6 after 50 km of fibre transmission. QML-based modulation policies achieve throughput levels up to 120 Mbps in low-error regimes, with secure quantum key distribution (QKD) tests showing sustainable key rates between 98 bps and 65 kbps. These results show high-throughput communication while minimising error rates. The study reveals the possibility of QML-enabled adaptive modulation in enhancing reliable, scalable, and secure quantum communication infrastructures.
Wireless communication systems face increasing challenges in resource allocation, latency reduction, scalability, and adaptation because modern networks generate high-dimensional, nonlinear, and rapidly changing data. This work presents a hybrid Quantum Neural Network (QNN)-driven framework for intelligent resource optimization in dynamic wireless communication environments. The proposed framework integrates variational quantum circuits with classical optimization techniques to support adaptive learning and efficient decision-making. A modular architecture consisting of User, Database, and Operator modules enables secure data acquisition, storage, preprocessing, continuous model training, and operator-driven optimization. Quantum Neural Networks are employed as the primary learning mechanism, while hybrid quantum-classical optimization supports stable parameter training. Quantum annealing is incorporated for complex tasks such as channel allocation, bandwidth management, and resource scheduling. The framework is designed to improve allocation accuracy, reduce latency, enhance scalability, reliability, security, and resource utilization. Continuous learning further enables adaptation to changing traffic patterns, user requirements, workloads, and network conditions. Keywords: Quantum Neural Network, Wireless Communication, Resource Optimization, Quantum Computing, Hybrid Quantum-Classical Learning, Resource Allocation.
The convergence of Sixth-Generation (6G) wireless networks and the Internet of Things (IoT) demands unprecedented levels of performance, scalability, and security. Traditional architectures are increasingly inadequate in addressing the computational and security challenges posed by massive IoT connectivity, ultra-low latency, and high data throughput. This paper proposes a novel quantum-assisted architecture that integrates quantum computing and quantum communication principles to enhance the efficiency and security of 6G-enabled IoT systems. The framework leverages quantum key distribution (QKD), quantum machine learning (QML), and entanglement-assisted routing to provide end-to-end encryption, intelligent resource allocation, and resilient data transmission. Our simulation results and comparative analysis demonstrate significant improvements in network throughput, latency, and security resilience compared to classical 6G-IoT architectures. This research establishes a foundational step toward realising secure and intelligent next-generation wireless networks through the integration of quantum technology.
Quantum Machine Learning (QML) has emerged as a transformative paradigm that integrates the computational advantages of quantum computing with the adaptive intelligence of machine learning to solve complex optimization problems in smart computing systems. Traditional optimization techniques often struggle with high-dimensional search spaces, combinatorial complexity, computational overhead, and scalability limitations in applications such as intelligent transportation, healthcare analytics, smart manufacturing, cybersecurity, cloud computing, and Internet of Things (IoT) infrastructures. QML approaches exploit quantum superposition, entanglement, and quantum parallelism to accelerate optimization tasks and enhance predictive performance beyond classical machine learning capabilities. This research proposes a hybrid Quantum Machine Learning optimization framework that combines Variational Quantum Circuits (VQCs), Quantum Neural Networks (QNNs), and classical deep learning models to address resource allocation, task scheduling, energy optimization, and intelligent decision-making in smart computing environments. The proposed framework integrates quantum-enhanced feature extraction, adaptive optimization layers, and reinforcement-based learning mechanisms to improve convergence speed and computational efficiency. Experimental analysis is performed using benchmark optimization datasets and simulated quantum environments to evaluate accuracy, execution time, energy efficiency, and scalability. Comparative results demonstrate that the proposed QML-based framework achieves superior optimization accuracy, reduced computational latency, and improved resource utilization when compared with traditional machine learning and classical metaheuristic optimization methods. Furthermore, the study highlights the practical significance of hybrid quantum-classical architectures in next-generation intelligent systems where large-scale optimization and real-time decision support are critical. The findings suggest that QML can significantly contribute to the advancement of sustainable, adaptive, and high-performance smart computing ecosystems while opening new directions for future quantum-aware intelligent infrastructures.
The rapid evolution of Software-Defined Radio (SDR) technology, coupled with advancements in quantum computing, has significantly enhanced contemporary communication systems. This paper presents a novel Store & Forward Repeater system for Push-to-Talk over Cellular (PTToC), integrating SDR with quantum technologies to enhance security and performance. Traditional repeaters encounter limitations in security, latency, and data integrity. Our system leverages SDR’s flexibility and reconfigurability, enabling dynamic adjustments in frequency and modulation to optimize communication between distant nodes in mobile networks. The system incorporates Quantum Key Distribution (QKD) for secure data transmission, ensuring that any interception attempts are detected, thereby providing theoretically unbreakable encryption. The Store & Forward Repeater stores messages when the destination is unreachable and forwards them securely once reconnected, minimizing data loss. Our testing demonstrated substantial improvements in latency (up to 70% reduction), error rate, and data security over conventional PTToC systems. We also provide a comprehensive comparison with conventional communication systems, highlighting the advantages of integrating quantum cryptography with SDR. Numerical analysis underscores the efficiency gains achieved through QKD over traditional encryption methods. Finally, we discuss future research directions, including quantum-assisted modulation schemes and the integration of 5G networks to further enhance communication in mission-critical applications. This work establishes the groundwork for scalable, secure communication systems with applications in public safety, military, and emergency response scenarios.
The advent of 6G wireless communication promises improvements in signal coverage, data rates, and latency, addressing increased connectivity demands due to the proliferation of 5G, IoT, and augmented reality. This paper introduces a Quantum-Secured IoT Communication Framework designed for 6G Cognitive Radio Networks (CRNs) in order to cater to the growing need for dependable and protected connectivity. Noteworthy aspects of this framework encompass dual-layer authentication utilizing Quantum Key Distribution (QKD) and Public Key Infrastructure (PKI), secured spectrum access regulations, and efficient beamforming strategies. Moreover, the framework employs a Reinforcement Learning-based Ensemble Regression (RL-ER) model for spectrum sensing and a Multi-Layer Perceptron with Kalman Filter (MLP-KF) for Channel State Information (CSI) prediction. Simulations demonstrate that the framework significantly improves prediction accuracy, encryption and decryption times, and error rates, thereby enhancing IoT network performance with better signal coverage, reduced latency, and robust security.