The rapid development of quantum machine learning (QML) has created new opportunities for malware detection and classification. However, the effectiveness of QML-based malware research depends strongly on the availability and suitability of open-source malware datasets. Although many publicly accessible malware datasets have been designed for classical machine learning, their features and limitations have not been systematically scrutinised on quantum computing grounds. This short review provides a structured overview of publicly available open-source malware datasets: EMBER (Endgame Malware BEnchmark for Research), DREBIN Android malware dataset, CIC-MalMem-2022 (Canadian Institute for Cybersecurity Malware Memory Analysis Dataset 2022), and VirusShare, and evaluates their suitability for quantum machine learning applications. The datasets are analysed based on data modality, feature representation, mode of labelling, and accessibility. Key research issues are identified, including high dimensionality, class imbalance, uncertainty of labels, and dataset bias. Particular attention is given to limitations posed by near-term quantum devices, such as qubit availability, circuit depth restrictions, and data encoding overhead. Among the reviewed datasets, DREBIN offers the most favourable characteristics for near-term QML experimentation. Its sparse binary feature structure, after reduction to a 126-feature subset or via principal component analysis (PCA) to 15 - 50 components, yields compact vectors compatible with current qubit constraints. EMBER and VirusShare present the greatest encoding challenges owing to their very high dimensionality and lack of standardised feature representations. Angle encoding and hybrid quantum-classical approaches are identified as the most practical encoding strategies for current NISQ (Noisy Intermediate-Scale Quantum) devices. A five-criterion evaluation framework, covering feature dimensionality, encoding feasibility, dataset size, label quality, and preprocessing requirements, is proposed to guide dataset selection. The findings underscore the urgent need for purpose-built benchmark datasets designed with quantum hardware constraints in mind.
The rapid development of quantum machine learning (QML) has created new opportunities for malware detection and classification. However, the effectiveness of QML-based malware research depends strongly on the availability and suitability of open-source malware datasets. Although many publicly accessible malware datasets have been designed for classical machine learning, their features and limitations have not been systematically scrutinised on quantum computing grounds. This short review provides a structured overview of publicly available open-source malware datasets: EMBER (Endgame Malware BEnchmark for Research), DREBIN Android malware dataset, CIC-MalMem-2022 (Canadian Institute for Cybersecurity Malware Memory Analysis Dataset 2022), and VirusShare, and evaluates their suitability for quantum machine learning applications. The datasets are analysed based on data modality, feature representation, mode of labelling, and accessibility. Key research issues are identified, including high dimensionality, class imbalance, uncertainty of labels, and dataset bias. Particular attention is given to limitations posed by near-term quantum devices, such as qubit availability, circuit depth restrictions, and data encoding overhead. Among the reviewed datasets, DREBIN offers the most favourable characteristics for near-term QML experimentation. Its sparse binary feature structure, after reduction to a 126-feature subset or via principal component analysis (PCA) to 15 - 50 components, yields compact vectors compatible with current qubit constraints. EMBER and VirusShare present the greatest encoding challenges owing to their very high dimensionality and lack of standardised feature representations. Angle encoding and hybrid quantum-classical approaches are identified as the most practical encoding strategies for current NISQ (Noisy Intermediate-Scale Quantum) devices. A five-criterion evaluation framework, covering feature dimensionality, encoding feasibility, dataset size, label quality, and preprocessing requirements, is proposed to guide dataset selection. The findings underscore the urgent need for purpose-built benchmark datasets designed with quantum hardware constraints in mind.
Quantum Machine Learning (QML) has become an important research area in recent years due to its potential to overcome the computational bottlenecks faced by classical machine learning methods. In this study, only SCI/SCI-Expanded journal articles published between 2023 and 2025 and accepted as early access for 2026 are considered, and 30 such articles are systematically examined to provide a comprehensive assessment of the current state of QML methods. The reviewed literature covers variational quantum circuits, quantum kernel methods, hybrid quantum–classical models, quantum annealing approaches, federated QML architectures, data encoding strategies, and adversarial robustness analyses. The findings indicate that QML models can exhibit stronger representational capacity and higher generalization performance compared to classical methods, particularly on high-dimensional, noisy, or small sample datasets. Reported results in domains such as healthcare, chemistry, energy systems, cybersecurity, blockchain, and finance reveal the diversity of QML applications and its interdisciplinary potential. Nevertheless, the limited qubit capacity of current NISQ hardware, constraints on circuit depth, and noise-related issues remain fundamental factors that restrict the scalability of QML models. This study synthesizes methodological trends, identifies open research problems, and proposes a directional roadmap for future QML research focused on hardware-aware circuit design, efficient data encoding schemes, and robust learning frameworks.
Classical machine learning (CML) has been extensively studied for malware classification. With the emergence of quantum computing, quantum machine learning (QML) presents a paradigm-shifting opportunity to improve malware detection, though its application in this domain remains largely unexplored. In this study, we investigate two hybrid quantumclassical models - a Quantum Multilayer Perceptron (QMLP) and a Quantum Convolutional Neural Network (QCNN), for malware classification. Both models utilize angle embedding to encode malware features into quantum states. QMLP captures complex patterns through full qubit measurement and data reuploading, while QCNN achieves faster training via quantum convolution and pooling layers that reduce active qubits. We evaluate both models on five widely used malware datasets -API-Graph, EMBER-Domain, EMBER-Class, AZ-Domain, and AZ-Class, across binary and multiclass classification tasks. Our results show high accuracy for binary classification95−96%on API-Graph, 91-92% on AZ-Domain, and77%on EMBER-Domain. In multiclass settings, accuracy ranges from 91.6-95.7% on API-Graph, 41.7-93.6% on AZ-Class, and 60.7-88.1% on EMBER-Class. Overall, QMLP outperforms QCNN in complex multiclass tasks, while QCNN offers improved training efficiency at the cost of reduced accuracy.
Intrusion Detection Systems (IDSs) are a cornerstone of modern cybersecurity; however, their performance is increasingly constrained by high-dimensional network traffic and the scalability limitations of classical machine learning approaches. Quantum Machine Learning (QML) has recently emerged as a promising paradigm by leveraging quantum feature spaces and hybrid quantum–classical optimization. In this work, we present a unified and reproducible benchmarking framework for QML-based intrusion detection under realistic Noisy Intermediate-Scale Quantum (NISQ) constraints. The proposed framework integrates hybrid Quantum Neural Networks (QNNs) and Quantum Support Vector Machines (QSVMs) within a consistent experimental pipeline, enabling a systematic comparison of quantum models, embedding strategies, and dimensionality reduction techniques. Experiments are conducted on the NSL-KDD benchmark dataset for both binary (normal versus attack) and multiclass intrusion detection tasks. Multiple quantum embedding strategies, including angle, amplitude, and Instantaneous Quantum Polynomial (IQP) embeddings, are evaluated in conjunction with PCA-based dimensionality reduction to analyze trade-offs between expressivity, circuit complexity, and computational cost. The proposed models are benchmarked against strong classical baselines under identical preprocessing and evaluation conditions. The results demonstrate that QNN- and QSVM-based IDS models achieve competitive and, in several scenarios, superior performance compared to classical approaches, particularly in multiclass settings characterized by nonlinear and imbalanced data distributions. In addition, we provide an in-depth analysis of scalability limitations, training complexity, and NISQ feasibility, highlighting practical considerations for real-world deployment. To ensure full reproducibility, all implementation details, datasets, and experimental configurations are publicly available at: https://github.com/raidanis/QML-IDS-NSLKDD. These findings position hybrid QML as a promising complementary paradigm for next-generation intrusion detection systems, particularly in low-dimensional and high-complexity feature spaces.
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.
Emerging cyber threats demand novel detection and analysis paradigms that classical machine learning approaches are increasingly unable to address at requisite scale and speed. Quantum Machine Learning (QML), leveraging quantum mechanical phenomena such as superposition and entanglement, has emerged as a promising framework for cyber threat intelligence (CTI). This scoping review systematically examines 24 published works from 2020 to 2025 following the PRISMA-ScR protocol. We classify QML frameworks like Quantum Support Vector Machines (QSVM), Quantum Neural Networks (QNN), Variational Quantum Circuits (VQC), and Quantum Convolutional Neural Networks (QCNN) across four primary CTI domains: intrusion detection, malware classification, anomaly detection, and phishing detection. Results show that QML models achieved up to 98% accuracy in intrusion detection and 97% in IoT anomaly detection on curated benchmark datasets. Critical caveats apply: 83.3% of studies relied exclusively on quantum simulators rather than real hardware, most benchmarks used datasets predating 2017, and the classical pre-processing and post-processing overheads in hybrid architectures substantially qualify reported speedup claims. We assess hardware constraints in Noisy Intermediate-Scale Quantum (NISQ) devices, data encoding bottlenecks, and missing quality benchmarks. NIST post-quantum cryptography standards (FIPS 203-205, August 2024) and IBM quantum hardware milestones are discussed as contextual anchors. A structured research agenda concludes the review.
Phishing remains a major cybersecurity threat, yet the application of quantum machine learning (QML) to phishing detection is still at an early stage. This study presents a systematic literature review aimed at providing a concise overview of existing QML-based approaches for phishing detection, identifying methodological trends, limitations, and future research directions. A PRISMA-guided review protocol was applied to peer-reviewed journal and conference articles published between 2021 and 2025, retrieved from major scientific databases. Eligible studies were analyzed in terms of QML models, feature encoding strategies, experimental settings, evaluation metrics, and study quality using an adapted Newcastle–Ottawa Scale. The results indicate that current research is limited in volume and largely focuses on hybrid quantum–classical models, particularly quantum support vector machines and variational quantum classifiers. Reported performance is highly dependent on encoding methods, circuit depth, and simulator-based experimentation, with few studies evaluating real quantum hardware. Common challenges include small datasets, lack of external validation, hardware noise, scalability constraints, and the absence of standardized benchmarks. Overall, the review suggests that QML for phishing detection remains exploratory and is not yet competitive with mature classical approaches, but it holds potential as an experimental research direction, provided that future studies address robustness, reproducibility, and practical deployment constraints.