Communication networks are managed more and more by using artificial intelligence. Anomaly detection, network monitoring and user behaviour are areas where machine learning offers advantages over more traditional methods. However, computer power is increasingly becoming a limiting factor in machine learning tasks. The rise of quantum computers may be helpful here, especially where machine learning is one of the areas where quantum computers are expected to bring an advantage. This paper proposes and evaluates three approaches for using quantum machine learning for a specific task in mobile networks: indoor–outdoor detection. Where current quantum computers are still limited in scale, we show the potential the approaches have when larger systems become available.
Communication networks are managed more and more by using artificial intelligence. Anomaly detection, network monitoring and user behaviour are areas where machine learning offers advantages over more traditional methods. However, computer power is increasingly becoming a limiting factor in machine learning tasks. The rise of quantum computers may be helpful here, especially where machine learning is one of the areas where quantum computers are expected to bring an advantage. This paper proposes and evaluates three approaches for using quantum machine learning for a specific task in mobile networks: indoor–outdoor detection. Where current quantum computers are still limited in scale, we show the potential the approaches have when larger systems become available.
There is a growing trend in using machine learning techniques for detecting environmental context in communication networks. Machine learning is one of the promising candidate areas where quantum computing can show a quantum advantage over their classical algorithmic counterpart on near term Noisy Intermediate-Scale Quantum (NISQ) devices. The goal of this paper is to give a practical overview of (supervised) quantum machine learning techniques to be used for indooroutdoor detection. Due to the small number of qubits in current quantum hardware, real application is not yet feasible. Our work is intended to be a starting point for further explorations of quantum machine learning techniques for indoor-outdoor detection.
With the potential for quantum computing to completely transform cybersecurity, quantum machine learning is becoming a ground-breaking technology. Cyber-attacks have been successfully countered by traditional network defense systems, which mostly use conventional machine learning (ML) techniques. However, the growing complexity of assaults and the exponential expansion of network data reveal the shortcomings of traditional methods, especially with regard to speed and scalability. By utilizing quantum algorithms like Quantum Support Vector Machines (QSVM), Quantum Neural Networks (QNN), and Variational Quantum Circuits (VQC), Quantum Machine Learning (QML) presents a viable substitute. These methods are especially well-suited for real-time network defense scenarios since they show the capacity to handle high-dimensional data, identify complex patterns, and increase computational efficiency. In this research, the effectiveness of QML techniques in network defense is thoroughly examined and contrasted with that of traditional machine learning techniques. The study emphasizes how QML may surpass conventional models in terms of accuracy, scalability, and resilience against sophisticated threats by concentrating on use cases like intrusion detection, malware analysis, and anomaly detection. The necessity for hybrid quantum-classical models and the present constraints of quantum hardware are among the difficulties that are discussed in the study. This study highlights the revolutionary potential of QML in strengthening network defense mechanisms and identifies crucial areas for further research, paving the road for a secure digital future by combining theoretical ideas with experimental discoveries.
This research explores the integration of quantum computing and machine learning for anomaly detection in critical infrastructure sensor networks. Anomaly detection is crucial for maintaining the reliability and security of these networks, where early detection of faults or intrusions can prevent catastrophic failures. Traditional machine learning methods often struggle with the high-dimensional and complex data generated by these systems. Hybrid quantum approaches, combining quantum algorithms with classical machine learning models, offer a promising solution by leveraging quantum computational advantages, such as quantum feature mapping and kernel methods. This paper investigates how these hybrid models can improve detection accuracy, reduce processing time, and handle large-scale data more efficiently. The potential of quantum machine learning for anomaly detection in critical infrastructure is discussed, along with the challenges posed by current quantum hardware limitations and future directions for research.
Network traffic analysis is essential for modern communication systems, focusing on tasks like traffic classification, prediction, and anomaly detection. While classical Machine Learning (ML) and Deep Learning (DL) methods have proven effective, their scalability and real-time performance can be limited by evolving traffic patterns and computational demands. Quantum Machine-Learning (QML) offers a promising alternative by utilizing quantum computing's parallelism. This paper examines QML's application in mobile traffic classification, comparing classical methods such as Multi-layer Perceptron (MLP) and Convolutional Neural Networks (CNNs) with Quantum Neural Networks (QNNs) using different embedding types. Our experiments, conducted on the MIRAGE-COVID-CCMA-2022 dataset, show that QNNs achieve competitive performance, indicating QML's potential for efficient large-scale traffic classification in future networks.
Two of the most disruptive technologies of the 21st century are quantum computing and artificial intelligence. Their intersection has led to the emergence of a new discipline referred to as Quantum Machine Learning (QML), which aims to enhance the capabilities of classical machine learning by leveraging the computational advantages of quantum devices. This paper provides a survey of the most advanced Quantum Machine Learning (QML) algorithms, including Quantum Support Vector Machines (QSVMs), Quantum k-nearest Neighbours (QkNN), Quantum Principal Component Analysis (QPCA), Quantum Neural Networks (QNNs), and Quantum Reinforcement Learning (QRL). The theoretical and practical status, as well as the empirical performance, of these algorithms, were summarised using a structured review method. The findings reveal a potential for speed-ups in classification, clustering, and optimisation among a range of applications, particularly for perfect quantum systems. However, hardware constraints, software irregularities, and training issues, such as barren plateaus, have limited the practical utility of this approach. Applications of QML in areas such as disaster preparedness and management, drug discovery, environmental sustainability, urban planning methodology, NLP, and finance demonstrate both the potential and current limitations of QML, with most applications still at the proof-of-concept level. In this review, we conclude that QML could be revolutionary, but its feasibility ultimately relies on improvements in physical hardware, the robustness of algorithms, and the standardisation of benchmarks.