A systematic review of Quantum Machine Learning techniques for phishing detection, synthesizing existing studies to extract methodological patterns and design principles rather than benchmark performance. The review examines QML paradigms, feature encoding strategies, and the impact of Noisy Intermediate-Scale Quantum constraints on learning behavior and feasibility. It highlights fragmentation, scalability limits, and limited practical gains over classical methods, and outlines research directions toward more robust, theoretically grounded, and application relevant quantum approaches for cybersecurity.
A systematic review of Quantum Machine Learning techniques for phishing detection, synthesizing existing studies to extract methodological patterns and design principles rather than benchmark performance. The review examines QML paradigms, feature encoding strategies, and the impact of Noisy Intermediate-Scale Quantum constraints on learning behavior and feasibility. It highlights fragmentation, scalability limits, and limited practical gains over classical methods, and outlines research directions toward more robust, theoretically grounded, and application relevant quantum approaches for cybersecurity.
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.
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.
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.
Quantum Machine Learning (QML) has emerged as a promising interdisciplinary field that combines quantum computing with machine learning to address complex computational problems. This survey provides a comprehensive overview of the theoretical foundations, unified taxonomy, and key methodologies in QML. We discuss quantum data encoding techniques, variational quantum models, neural-inspired quantum architectures, and quantum data learning approaches, along with their associated challenges and limitations. The survey further examines evaluation strategies, the concept of quantum advantage, and the role of current quantum hardware and software platforms in advancing QML research. In addition, major application domains and the growing importance of trustworthy QML, including interpretability, robustness, and security, are explored. Finally, we highlight open challenges and future research directions to provide insights into the evolving landscape of Quantum Machine Learning.
Quantum Machine Learning (QML), a confluence of quantum computing and classical machine learning, represents a revolutionary paradigm with transformative potential. This systematic review explores the landscape of QML by investigating its underlying principles, methodologies, diverse applications, challenges, and ethical considerations. Beginning with an examination of fundamental quantum computing principles, the review navigates through various QML methodologies, comparing them with classical counterparts. Real-world applications, ranging from quantum-enhanced optimization to drug discovery, are scrutinized, showcasing the practical implications of QML across industries. The paper systematically identifies challenges, including quantum hardware constraints and ethical considerations, while offering insights into current limitations and future research directions. A comparative analysis benchmarks QML against classical machine learning approaches, providing a nuanced understanding of its strengths and limitations. Ethical considerations underscore the importance of responsible AI practices in the integration of QML. The review concludes by identifying research gaps and suggesting future directions, emphasizing the need for continued exploration in this dynamic intersection of quantum computing and machine learning. This comprehensive exploration serves as a valuable resource for researchers, practitioners, and decision-makers seeking insights into the current state and transformative potential of Quantum Machine Learning.
ABSTRACT Context The rapid increase in cyber threats, coupled with increasingly sophisticated attack strategies and the exponential growth of data in recent years, has exposed significant limitations in classical machine learning, rule‐based, and signature‐based defense mechanisms. These approaches are often unable to scale or adapt effectively to evolving threat landscapes. Objective As a result, Quantum Machine Learning (QML) has emerged as a promising alternative, leveraging computational principles from quantum mechanics. QML enables more expressive encoding and efficient processing of high‐dimensional data, offering potential advantages for complex cybersecurity tasks. Method This survey provides a comprehensive overview of QML techniques relevant to the domain of security, such as Quantum Neural Networks (QNNs), Quantum Support Vector Machines (QSVMs), Variational Quantum Circuits (VQCs), and Quantum Generative Adversarial Networks (QGANs), and discusses the contributions of this paper in relation to existing research in the field and how it improves over them. Results It also maps these methods across supervised, unsupervised, and generative learning paradigms, as well as to core cybersecurity tasks, including intrusion and anomaly detection, malware and botnet classification, and encrypted‐traffic analytics. It also discusses their application in cloud computing security, where QML can enhance secure, scalable operations. Conclusion Many limitations of QML in the domain of cybersecurity have also been discussed, along with the directions for addressing them.