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.
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.
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.
This paper presents a novel approach to quantum error correction (QEC) and adaptive quantum channel mapping (QECM) designed to enhance the robustness of quantum information transmission through noisy channels. The core idea is to leverage the redundancy inherent in QEC codes and combine it with a machine learning-based adaptive mapping algorithm. Unlike traditional methods that rely on pre-characterized channel models, this approach dynamically optimizes the QEC encoding and channel mapping strategies based on real-time channel feedback. The system utilizes an error correction matrix derived from the chosen QEC code to construct a quantum channel model. A neural network, specifically a variational quantum neural network (VQNN), is then trained to learn the optimal quantum channel mapping policy. This dynamic optimization addresses the limitations of fixed channel models and promises improved performance in practical quantum communication scenarios. The effectiveness of the QECM framework is demonstrated through theoretical analysis and a conceptual model, paving the way for robust long-distance quantum communication. The system's adaptability allows it to cope with unknown or time-varying channel characteristics, a critical factor for successful quantum communication implementations. The ultimate goal is to achieve efficient and reliable quantum information transfer, regardless of the underlying channel noise.
Quantum Machine Learning when fused with IoT systems holds capabilities of transforming the communication networks by providing optimum effectiveness, security and scalability. With the increased adoption of IoT systems, the monitoring techniques and data management, the need to facilitate real-time communication, and managing resources effectively become observations for IoT system developments. The computed-based machine learning algorithms do help in solving these problems, but these algorithms have their limitations due to the traditional classic systems. Regarding the intentions of this research, it is fundamental to highlight that the QML techniques will be centered on the capability that quantum algorithms have over processing and analyzing big amounts of data in IoT scenarios. This paper has demonstrated by means of QML how quantum parallelism and entanglement can enhance the key aspects of IoT systems, such as decisionmaking, information transfer, and correction. Also, quantum cryptographic techniques contain higher reliability providing confidentiality and indefectibility of sent information. This research focuses on diverse quantum algorithms including quantum clustering, classification, and reinforcement learning in IoT communication. The conducted simulations and practical case studies in the research show that QML is applicable and may provide benefits when integrated into IoT networks including more efficient networks, increased throughput, and security. These results point to the potential that QML might assume in defining the future of IoT communications, specifically in highly necessary and utilizing applications.
The integration of Quantum Key Distribution (QKD) with Machine Learning (ML) has developed a high-end, secure model for accurate data transmission and network security. Many existing systems face several issues due to advances in quantum computing. Thus, integrating these models improved the efficiency, accuracy, and feasibility of QKD systems. In this paper, an Advanced Learning Approach (ALA) is combined with the Quantum Key Distribution Framework to refine key generation, error correction, and intrusion detection in quantum communication channels. The proposed ALA also used reinforcement learning algorithms to predict quantum channel fluctuations, dynamically adjust key exchange parameters, and strengthen authentication protocols. Simulation results demonstrate improved key rates, reduced Quantum Bit Error Rates (QBER), and enhanced resilience against eavesdropping attacks. The study contributes to the realization of practical, scalable, and intelligent quantum-secure communication systems—bridging the gap between theoretical quantum security and real-world network deployment.
Quantum Key Distribution (QKD) offers theoretically unbreakable security by leveraging quantum mechanics. However, practical implementation is challenged by environmental vulnerabilities, noise, and hardware imperfections. Recently, Machine Learning (ML) has emerged as a powerful tool to address these limitations and enhance the real-world viability of QKD systems. In this survey, we review ML techniques applied to improve QKD security and performance across five applications. First, parameter optimization, covering signal calibration, polarization alignment, phase stabilization, modulation state tuning, and post-processing enhancements to maximize secure key generation and minimize error rates. Second, attack detection, where ML models identify and classify quantum threats such as photon-number-splitting and Trojan-horse attacks. Third, protocol selection, leveraging ML to dynamically choose QKD protocols based on operational conditions. Fourth, key performance prediction of core metrics such as Secret Key Rate (SKR) and Quantum Bit Error Rate (QBER). Finally, quantum network management, optimizing large-scale QKD deployments through intelligent routing, node management, and resource allocation. Performance improvements are evaluated using accuracy, reduced QBER, and increased SKR. While ML shows significant potential for finance, government, and defense applications, challenges remain in scalability, computational demands, and real-world testing. Ongoing work should focus on lightweight, generalizable models and standardized benchmarks for practical ML-enhanced QKD deployment.
Quantum Key Distribution (QKD) promises theoretically unbreakable encryption by using fundamental quantum physics principles. QKD has emerged as a contender in the next generation of encryption protocols as the demand for more secure communication grows in our linked society. However, practical QKD implementations confront obstacles such as noise, interception attempts, and flaws in quantum hardware. We investigate the possibilities of Machine Learning (ML) techniques to improve the efficiency, resilience, and security of QKD protocols in this study. We provide a novel system that uses deep learning models to optimize photon polarization states and hence increase the key generation rate. Our approach detects and mitigates eavesdropping attempts using unsupervised learning algorithms, substantially enhancing the security of quantum communications. To achieve security, the BB84 protocol makes use of fundamental quantum mechanics features. Furthermore, we use reinforcement learning to adaptively alter system settings in real time to account for the quirks and noise profiles of different quantum channels. In comparison to typical QKD setups, experimental results show that our ML-enhanced QKD system is more resilient to known quantum attacks and provides a more consistent and efficient key distribution rate. Our findings indicate that the interaction of quantum physics and machine learning can pave the way for more secure and practical quantum communication networks in the near future.