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IEEE International Mediterranean Conference on Communications and Networking (MeditCom)

From Features to Qubits: A Hybrid Learning Approach for Predicting SDN Link States

Muhammad Afaq

Published Jul 7, 2025• 1 citation
Publisher

Abstract

Software-Defined Networks (SDNs) significantly enhance network management through centralized control, programmability, and agility. However, accurate link prediction remains challenging due to the complexity and dynamic topology of SDNs. Classical machine learning techniques achieve high accuracy but can struggle to model subtle, nonlinear relationships in rapidly evolving networks. Quantum Machine Learning (QML) techniques exploit quantum phenomena to capture these complex patterns, yet current quantum hardware limitations restrict their standalone applicability. To address these challenges, this paper proposes a hybrid classical-quantum machine learning framework for SDN link prediction. Classical preprocessing, including normalization and feature selection, prepares high-quality input for quantum processing. The selected features are then mapped into quantum states using the ZZFeatureMap, enabling the Quantum Support Vector Machine (QSVM) to detect complex patterns through quantum-enhanced kernels. Rigorous experimentation using synthetic SDN datasets demonstrates that the hybrid QSVM achieves an area under the ROC curve (AUC) of 0.84, an average precision (AP) of 0.74, and an accuracy of 0.84—closely matching classical SVM and approaching the performance of Random Forest (AUC = 0.86, AP = 0.78, Accuracy = 0.88). The QSVM and classical SVM also demonstrate significantly lower training time ( 0.04s) compared to Random Forest ( 0.29s), highlighting the hybrid model's efficiency. These results provide strong evidence that quantum-enhanced techniques can effectively complement classical models, offering improved robustness and scalability for intelligent network systems.

DOI
10.1109/meditcom64437.2025.11104468

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Software-Defined Networks (SDNs) significantly enhance network management through centralized control, programmability, and agility. However, accurate link prediction remains challenging due to the complexity and dynamic topology of SDNs. Classical machine learning techniques achieve high accuracy but can struggle to model subtle, nonlinear relationships in rapidly evolving networks. Quantum Machine Learning (QML) techniques exploit quantum phenomena to capture these complex patterns, yet current quantum hardware limitations restrict their standalone applicability. To address these challenges, this paper proposes a hybrid classical-quantum machine learning framework for SDN link prediction. Classical preprocessing, including normalization and feature selection, prepares high-quality input for quantum processing. The selected features are then mapped into quantum states using the ZZFeatureMap, enabling the Quantum Support Vector Machine (QSVM) to detect complex patterns through quantum-enhanced kernels. Rigorous experimentation using synthetic SDN datasets demonstrates that the hybrid QSVM achieves an area under the ROC curve (AUC) of 0.84, an average precision (AP) of 0.74, and an accuracy of 0.84—closely matching classical SVM and approaching the performance of Random Forest (AUC = 0.86, AP = 0.78, Accuracy = 0.88). The QSVM and classical SVM also demonstrate significantly lower training time ( 0.04s) compared to Random Forest ( 0.29s), highlighting the hybrid model's efficiency. These results provide strong evidence that quantum-enhanced techniques can effectively complement classical models, offering improved robustness and scalability for intelligent network systems.
Software-Defined Networks (SDNs) with their centralized control system and enhanced programmability requires a sophisticated approach to predict link states in complex network topologies. While predictive performance of traditional machine learning (ML) models remains high on static data, they struggle under dynamic and imbalanced network behavior. Although Quantum Machine Learning (QML) offers a promising solution by leveraging quantum-enhanced kernels to discover non-linear patterns, current quantum hardware constraints limit its standalone applicability. This paper presents an extended hybrid classical-quantum learning framework for SDN link state prediction under both benign (stable) and attack (compromised) conditions, integrating classical preprocessing with quantum kernel embedding via the ZZFeatureMap. A tunable parameter α0,1 enables dynamic interpolation between classical Radial Basis Function (RBF) and quantum kernels. On a stratified 5,000-sample subset of the InSDN dataset, which contains both normal and attack traffic flows, the hybrid Quantum Support Vector Machine (QSVM) achieves 85% accuracy, 0.92 ROC–AUC, and 0.73 Average Precision (AP). Scalability experiments on 20,000 samples confirm stable performance using Nyström-approximated kernels. Comparative evaluations with existing core classical, gradient boosting, and deep learning approaches highlight hybrid QSVM’s tunable expressivity, controlled computational scaling, and robustness to data distribution shifts, demonstrating its potential for future quantum-enabled SDN analytics.
Recently, there has been growing attention on combining quantum machine learning (QML) with classical deep learning approaches as computational techniques are key to improving the performance of image classification tasks.This study presents a hybrid approach that uses ResNet-50 (Residual Network) for feature extraction and Quantum Support Vector Machines (QSVM) for classification in the context of potato disease detection. Classical machine learning as well as deep learning models often struggle with high-dimensional and complex datasets necessitating advanced techniques like quantum computing to improve classification efficiency.In our research, we use ResNet-50 to extract deep feature representations from RGB images of potato diseases. These features are then subjected to dimensionality reduction using PCA (Principal Component Analysis). The resulting features are processed through QSVM models which apply various quantum feature maps—such as ZZ, Z and Pauli-X to transform classical data into quantum states. To assess the model’s performance we compared it with classical ML such as Support Vector Machine (SVM) and Random Forest (RF) using 5-fold stratified cross-validation for a comprehensive evaluation. The experimental results demonstrate that the Z-feature map-based QSVM outperforms classical models achieving an accuracy of 99.23% surpassing both SVM and RF models.This research highlights the advantages of integrating quantum computing into image classification and also provides disease detection solution into the potential of hybrid quantum-classical model.
Quantum machine learning (QML) algorithms for intrusion detection systems in software-defined networks are investigated, and their effectiveness is compared with their classical machine learning methods. The University of Nevada - Reno intrusion detection dataset (UNR-IDD) is used to evaluate different QML models, including quantum k-nearest neighbors (QKNNs), quantum support vector machines (QSVMs), quantum neural networks (QNNs), and hybrid quantum neural networks (HQNNs). These models were tested with quantum simulators to evaluate their potential advantages in processing complex datasets. The results show that HQNN and QSVM have higher accuracy than their classical SVM and NN counterparts. This study shows the potential of leveraging QML to enhance precision. References to other works that dive into efficiency and complexity are included.
Software Requirement Risk Analysis is crucial for detecting potential failures early in the Software Development Life Cycle (SDLC). Existing machine learning (ML) and deep learning (DL) methods provide promising results, but they often struggle with capturing complex dependencies in requirement data. This paper introduces a novel hybrid Quantum-Classical Machine Learning (QML) framework to improve the accuracy and reliability of requirement risk classification. The dataset was preprocessed using label encoding, Synthetic Minority Oversampling Technique (SMOTE), ANOVA F-test (SelectKBest) for feature selection, and feature scaling to enable quantum circuit integration. Strongly entangling quantum circuits were implemented using the Qiskit framework to generate quantumenhanced features, which were fused with classical features. Four hybrid models-RF + QML, LGBM + QML, LR + QML, and Q-Kernel + MLP-were trained and evaluated. Experimental results show that the proposed Q-Kernel + MLP model achieved the best performance with 97.76%97.76 \%97.76% accuracy, 97.92%97.92 \%97.92% precision, 97.76%97.76 \%97.76% recall, and an F 1 -score of 97.77%97.77 \%97.77%, outperforming classical baselines by up to 4%4 \%4%. These findings highlight the potential of quantum-enhanced learning in software engineering, offering a scalable and robust solution for early risk detection and supporting more reliable project planning and software quality assurance.
Quantum Machine Learning (QML) provides a pathway for analysing nonlinear relationships through quantum- enhanced feature spaces. Although quantum models are theoreti- cally well suited for high-complexity data, current hardware and simulator limitations restrict their use to small datasets. For this reason, the original large Web Phishing Detection Dataset was reduced to 200 representative samples through dimensionality reduction, allowing controlled experimentation and enabling evaluation of quantum behaviour with manageable computational cost. QML research commonly follows this approach, testing algorithms on small datasets first, because quantum feature maps can generate exponentially expressive Hilbert-space embeddings even from limited inputs. In this study, multiple Quantum Support Vector Machine (QSVM) models were implemented using fidelity-based quantum kernels and Pauli feature maps. Models with 2 to 10 qubits were evaluated to observe how circuit width affects performance. The two-qubit QSVM achieved the best quantum result with 72 % accuracy, whereas deeper circuits showed declining performance, indicating overfitting and reduced generalization on limited data. In contrast, the classical SVM attained 97 % accuracy with significantly lower computational effort. These findings highlight the challenges and potential of QML for cybersecurity tasks. Future work will explore circuit- expressivity tuning, optimized quantum kernels, hybrid quantum- classical approaches, and scalability to larger phishing datasets as quantum hardware evolves.
This research presents a comparative evaluation of classical and quantum machine learning models applied to the Bridge dataset. Classical algorithms like Support Vector Machine, Random Forest, and Neural Networks are benchmarked against Quantum SVM, Quantum Random Forest, and Quantum Neural Networks using identical preprocessing and training conditions. Results indicate a consistent quantum advantage, with quantum models achieving higher accuracy, stronger nonlinear feature separation, and improved minority-class detection. QSVM and QNN exhibit the most significant performance gains. Although quantum models require greater computational resources, the findings underscore the emerging effectiveness of quantum-enhanced learning for structural classification tasks in the NISQ era.