The detection of android malware by machine learning techniques continues to face challenges with the increasing diversity of malware. The classical machine learning approach based on Support Vector Machines (SVM) with radial basis function (RBF) kernel has been shown to perform reliably on static features such as permissions and API calls. Meanwhile, an alternative quantum machine learning approach based on Quantum Support Vector Machines (QSVM) has been introduced by replacing classical similarity measures with quantum kernels. However, the performance of QSVM under real-world noisy quantum environments remains unknown. In this work, we investigate the performance of QSVM for android malware detection by employing a stratified 150-sample subset of the TUANDROMD dataset. We reduce the feature space from 241 dimensions to four principal components according to current qubit limitations while retaining 59.4% of the total variance of the feature space. We compare the performance of QSVM with a classical machine learning approach based on RBF-SVM by simulating depolarizing gate errors and measurement noise via the Qiskit Aer package. The experimental results show that the classical SVM has 90.4% accuracy with a 5.6% false negative rate, while QSVM has only 83.7% under ideal simulation and gradually reduces with the increase in noise. The statistical analysis has proved that the gap between the two is statistically significant. In summary, although QSVM has shown its feasibility, it has failed to compete with the classical algorithms under current hardware constraints.
The detection of android malware by machine learning techniques continues to face challenges with the increasing diversity of malware. The classical machine learning approach based on Support Vector Machines (SVM) with radial basis function (RBF) kernel has been shown to perform reliably on static features such as permissions and API calls. Meanwhile, an alternative quantum machine learning approach based on Quantum Support Vector Machines (QSVM) has been introduced by replacing classical similarity measures with quantum kernels. However, the performance of QSVM under real-world noisy quantum environments remains unknown. In this work, we investigate the performance of QSVM for android malware detection by employing a stratified 150-sample subset of the TUANDROMD dataset. We reduce the feature space from 241 dimensions to four principal components according to current qubit limitations while retaining 59.4% of the total variance of the feature space. We compare the performance of QSVM with a classical machine learning approach based on RBF-SVM by simulating depolarizing gate errors and measurement noise via the Qiskit Aer package. The experimental results show that the classical SVM has 90.4% accuracy with a 5.6% false negative rate, while QSVM has only 83.7% under ideal simulation and gradually reduces with the increase in noise. The statistical analysis has proved that the gap between the two is statistically significant. In summary, although QSVM has shown its feasibility, it has failed to compete with the classical algorithms under current hardware constraints.
Cyberbullying has become much more common due to the proliferation of social media, which has devastating effects on users' mental health and relationships. Automatic identification of abusive textual content has therefore become an important research problem. Cyberbullying detection has seen extensive use of classical machine learning approaches, notably Support Vector Machines (SVMs); however, their effectiveness can be limited when handling high-dimensional and context-rich textual features. Recent developments in quantum computing introduce new computational paradigms that may enable more expressive feature representations through quantum-enhanced learning models. In this study, we explore the potential of a hybrid. Nowadays classical-quantum architecture to apply a Quantum Support Vector Machine (QSVM) to detect cyberbullying. Experiments were performed on a publicly available Kaggle dataset containing 35,824 labeled tweets. Before TF-IDF feature extraction, the text data underwent preprocessing via cleaning and tokenization. Dimension reduction was carried out using Principal Component Analysis (PCA) in respect of qubit constraints. Then, they were embedded into quantum states using angle embedding. The accuracy, precision, recall, and F1-score measures were used to compare QSVM models with various qubit configurations (2, 4, and 6), as well as a traditional SVM baseline, under an 80-20 stratified train-test split. The findings indicate that the quality of QSVM performance is highest with the increase in qubit numbers and the maximum accuracy of the quantum computing method is 60 percent according to six qubits, whereas the classical SVM achieves 67.5 percent. Although the quantum model currently incurs higher computational overhead due to simulator-based implementation, increasing qubit dimensionality enhances its ability to capture complex and non-linear linguistic patterns. These observations indicate that QSVM-based cyberbullying detection remains exploratory at present, with scope for further improvement as quantum hardware and algorithms advance.
Classical machine learning methods have demonstrated strong performance in email phishing detection, but their ability to generalize against sophisticated phishing behaviors remains limited. Recent advances in quantum machine learning suggest that quantum kernel methods may capture complex feature interactions more effectively. This paper presents a batchwise ensemble evaluation framework that independently trains classical Support Vector Machine (SVM) and Quantum Support Vector Machine (QSVM) learning models using identical datasets. Due to quantum computational constraints, training is performed on fixed-size data batches and final predictions are obtained through majority voting across batches. Experimental results show that the QSVM ensemble reduces false negatives by 79 % compared to classical SVM, although at significantly higher computational cost, highlighting both the potential and current limitations of quantum kernel methods in the NISQ era.
This study explores the application of quantum machine learning (QML) algorithms to enhance cybersecurity threat detection, particularly in the classification of malware and intrusion detection within high-dimensional datasets. Classical machine learning approaches encounter limitations when dealing with intricate, obfuscated malware patterns and extensive network intrusion data. To address these challenges, we implement and evaluate various QML algorithms, including Quantum Neural Networks (QNN), Quantum Support Vector Machines (QSVM), and hybrid Quantum Convolutional Neural Networks (QCNN) for malware detection tasks. Our experimental analysis utilized two datasets: the Intrusion dataset, comprising 150 samples with 56 memory-based features derived from Volatility framework analysis, and the ObfuscatedMalMem2022 dataset, containing 58,596 samples with 57 features representing benign and malicious software. Remarkably, our QML methods demonstrated superior performance compared to classical approaches, achieving accuracies of 95% for QNN and 94% for QSVM. These quantum-enhanced methods leveraged quantum superposition and entanglement principles to accurately identify complex patterns within highly obfuscated malware samples that were imperceptible to classical methods. To further advance malware analysis, we propose a novel real-time malware analysis framework that incorporates Quantum Feature Extraction using Quantum Fourier Transform, Quantum Feature Maps, and Classification using Variational Quantum Circuits. This system integrates explainable AI methods, including GradCAM++ and ScoreCAM algorithms, to provide interpretable insights into the quantum decision-making processes.
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.
Quantum machine learning (QML) has emerged as an important area for Quantum applications, although useful QML applications would require many qubits. Therefore our paper is aimed at exploring the successful application of the Quantum Support Vector Machine (QSVM) algorithm while balancing several practical and technical considerations under the Noisy Intermediate-Scale Quantum (NISQ) assumption. For the quantum SVM under NISQ, we use quantum feature maps to translate data into quantum states and build the SVM kernel out of these quantum states, and further compare with classical SVM with radial basis function (RBF) kernels. As data sets are more complex or abstracted in some sense, classical SVM with classical kernels leads to less accuracy compared to QSVM, as classical SVM with typical classical kernels cannot easily separate different class data. Similarly, QSVM should be able to provide competitive performance over a broader range of data sets including ``simpler'' data cases in which smoother decision boundaries are required to avoid any model variance issues (i.e., overfitting). To bridge the gap between ``classical-looking'' decision boundaries and complex quantum decision boundaries, we propose to utilize general shallow unitary transformations to create feature maps with rotation factors to define a tunable quantum kernel, and added regularization to smooth the separating hyperplane model. We show in experiments that this allows QSVM to perform equally to SVM regardless of the complexity of the data sets and outperform in some commonly used reference data sets.
Quantum Support Vector Regression (QSVR) has emerged as one of the most promising quantum machine learning algorithms for nonlinear regression on Noisy Intermediate-Scale Quantum (NISQ) devices. However, despite numerous simulation-based studies, experimental evaluations on physical quantum hardware remain relatively scarce. This work presents a comparative analysis of three regression approaches: classical Support Vector Regression (SVR) with a Radial Basis Function (RBF) kernel, classical SVR utilizing precomputed quantum kernels, and the native QSVR implementation available in the qiskit-machine-learning framework. Experiments were conducted on three benchmark regression datasets using three execution environments: a noiseless Qiskit Aer simulator, the IQM Adonis hardware-aware simulator, and the physical 5-qubit IQM Spark superconducting quantum computer (Odra 5) located at Wrocław University of Science and Technology. Multiple quantum feature maps and circuit depths were investigated, while model hyperparameters were optimized using grid search. The obtained results demonstrate that QSVR can be successfully executed on contemporary superconducting quantum hardware and, for selected datasets, achieves prediction accuracy comparable to or exceeding that of a classical RBF-based SVR. The experiments further reveal the significant influence of quantum hardware noise, feature map selection, and circuit depth on regression quality. Moreover, the implementation based on precomputed quantum kernels generally exhibits greater robustness on physical hardware than the native QSVR implementation. The presented benchmark provides an experimental assessment of QSVR under realistic execution conditions and contributes to the ongoing evaluation of quantum machine learning algorithms on current NISQ-era quantum computers.