Quantum machine learning (QML) has gained attention for its potential to leverage quantum advantages in processing high-dimensional data, capturing complex correlations, and exploring large solution spaces, beyond the limits of classical machine learning. In infrastructure risk, resilience, disruption, and recovery analysis, these capabilities position QML as a promising research direction with not yet realised applicability. This study examines whether QML studies move beyond computational novelty to address the substantive requirements of infrastructure-oriented disaster risk management. Using a systematic literature review of records from Scopus and Web of Science, the search retrieved 784 records before deduplication and retained 33 studies after staged screening and full-text eligibility assessment. Studies were evaluated through an infrastructure risk lens. The evidence base is narrow in sectoral scope, dominated by energy and power applications, and more developed in restoration, dynamic security, cyber-physical protection, operational instability, and disruption-oriented operational analysis than in hazard-explicit infrastructure disaster-risk assessment. Hybrid quantum-classical workflows predominate, while many studies rely on simulated, benchmarked, stylised, or case-specific data with limited validation and uneven reporting of data and preprocessing practices. Although selected studies show potential for restoration, classification, anomaly detection, optimisation, and adaptive coordination, the field remains partly connected to core infrastructure-risk concerns including hazard representation, vulnerability, interdependence, cascading disruption, continuity, and recovery dynamics. QML should therefore be understood not as a mature framework for infrastructure-oriented disaster risk management, but as a promising computational frontier with potential to advance the field through broader sectoral application, stronger empirical validation, multi-hazard integration, and benchmarking against classical methods.
Quantum machine learning (QML) has gained attention for its potential to leverage quantum advantages in processing high-dimensional data, capturing complex correlations, and exploring large solution spaces, beyond the limits of classical machine learning. In infrastructure risk, resilience, disruption, and recovery analysis, these capabilities position QML as a promising research direction with not yet realised applicability. This study examines whether QML studies move beyond computational novelty to address the substantive requirements of infrastructure-oriented disaster risk management. Using a systematic literature review of records from Scopus and Web of Science, the search retrieved 784 records before deduplication and retained 33 studies after staged screening and full-text eligibility assessment. Studies were evaluated through an infrastructure risk lens. The evidence base is narrow in sectoral scope, dominated by energy and power applications, and more developed in restoration, dynamic security, cyber-physical protection, operational instability, and disruption-oriented operational analysis than in hazard-explicit infrastructure disaster-risk assessment. Hybrid quantum-classical workflows predominate, while many studies rely on simulated, benchmarked, stylised, or case-specific data with limited validation and uneven reporting of data and preprocessing practices. Although selected studies show potential for restoration, classification, anomaly detection, optimisation, and adaptive coordination, the field remains partly connected to core infrastructure-risk concerns including hazard representation, vulnerability, interdependence, cascading disruption, continuity, and recovery dynamics. QML should therefore be understood not as a mature framework for infrastructure-oriented disaster risk management, but as a promising computational frontier with potential to advance the field through broader sectoral application, stronger empirical validation, multi-hazard integration, and benchmarking against classical methods.
The growing complexity and velocity of cyber threats in high-security environments such as defense, critical infrastructure, and intelligence networks necessitates a paradigm shift in threat detection capabilities. Traditional cybersecurity systems, including those enhanced by classical machine learning algorithms, often struggle to process and classify massive volumes of heterogeneous and encrypted data in real time. This shortcoming is particularly evident in the context of advanced persistent threats (APTs), polymorphic malware, and insider attacks, which require rapid adaptation and heightened sensitivity to anomalous behavior.Quantum Machine Learning (QML), an emerging interdisciplinary field at the intersection of quantum computing and artificial intelligence, presents a promising avenue for augmenting threat detection mechanisms. Leveraging quantum phenomena such as superposition and entanglement, QML models offer potential advantages in processing speed, pattern recognition, and feature space transformation that can outperform their classical counterparts in high-dimensional data analysis. This paper explores the application of QML to threat detection in high-security networks, proposing a hybrid quantum-classical framework that integrates quantum-enhanced classifiers such as quantum support vector machines and variational quantum circuits into existing detection pipelines.The study outlines a technical overview of quantum computing principles relevant to cybersecurity, critically evaluates existing detection architectures, and presents simulation-based case studies to assess performance metrics, including detection accuracy and false positive rates. It further examines the limitations of current quantum hardware, algorithmic constraints, and emerging ethical and operational considerations. The findings suggest that while QML is still constrained by hardware maturity and integration complexity, it holds transformative potential for proactive, intelligent, and adaptive cyber defense systems in high-stakes environments. This research contributes to ongoing efforts to future-proof cybersecurity infrastructure against both classical and post-quantum threat landscapes.
Quantum Machine Learning (QML) merges Quantum Computing (QC) and Artificial Intelligence (AI) to solve problems that are intractable by classical computers. At the same time, domains such as finance, healthcare, manufacturing, and transportation domains are transitioning into Internet of Things (IoT)-enabled cyber physical systems generating streams of heterogeneous data with high dimensionality and volume. This work presents a unified scientometric and qualitative review of QML across these sectors. A PRISMA-based screening is followed by a Keyword Co-occurrence Cluster Analysis (KCCA), a Keyword Burst Analysis (KBA), and a Citation Burst Analysis (CBA) using CiteSpace on a Scopus-derived corpus of literature. An evidence-based Technology Readiness Level (TRL) framework is introduced to assess the technology’s maturity. Our findings indicate that the volume of publications has increased after 2022, with healthcare dominating the volume and manufacturing the impact. Furthermore, a clear trend towards the development of hybrid quantum classical architectures is present across all domains. Security applications such as anomaly detection are the most developed QML entry point to IoT systems. All four domains are at a TRL of 3-4, indicating that QML research is still at the concept-validation stage. Challenges include hardware noise, barren plateaus, absence of standardized benchmarks, and encoding heterogeneous data from IoT devices for training QML applications.
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.
Quantum machine learning (QML) has emerged as a promising approach for analyzing the complex, high-dimensional data encountered in oncology, yet research in this area remains fragmented. This systematic literature review synthesizes current applications of QML in cancer care. Following PRISMA guidelines, six databases were searched for peer-reviewed English-language studies published between 2020 and 2026. Of the 212 records identified, 49 studies met the inclusion criteria after screening and quality assessment. The findings show that QML research is dominated by classification and detection tasks, while segmentation is beginning to emerge. Breast cancer and brain tumors are the most frequently investigated domains. Hybrid quantum-classical models, particularly quantum kernel methods, quantum neural networks, and quantum convolutional neural networks, are the predominant approaches. The main barriers to adoption are hardware limitations, including quantum noise, limited qubit availability, and reliance on simulators. Overall, QML in oncology remains in its early stages of development, with limited clinical validation, insufficient model interpretability, and little evidence of a clear quantum advantage. Future research should prioritize evaluation on real quantum hardware, larger and more diverse clinical datasets, standardized benchmarking against classical methods, and closer collaboration between computer scientists and oncology experts to facilitate clinical translation.
Quantum Machine Learning (QML) is emerging as a transformative technology in cybersecurity, particularly in anomaly detection for cyber security audits. Traditional machine learning models are effective but face scalability and efficiency limitations as cyber threats grow more sophisticated. QML, leveraging quantum computing’s ability to process and analyze large datasets in parallel, offers potential breakthroughs in identifying anomalous patterns that could signify cyber threats such as data breaches, insider threats, or unauthorized access. Content Analysis Research Methodology used in this research work. This paper explores the integration of QML into anomaly detection systems for cyber security audits, where detecting deviations from normal behavior is crucial. Quantum algorithms, particularly those based on Quantum Support Vector Machines (QSVM), Quantum Neural Networks (QNN), and Quantum Principal Component Analysis (QPCA) can enhance the detection of subtle anomalies that classical algorithms may overlook due to noise or the complex, high-dimensional nature of cyber data. The inherent properties of quantum computing, such as superposition and entanglement, allow for more efficient feature selection and optimization, potentially leading to faster and more accurate anomaly detection. The impact of implementing QML in cyber security audits is profound. First, it enhances detection capabilities by identifying anomalies with greater precision, reducing false positives, and improving response times to cyber incidents. Second, quantum algorithms’ ability to manage exponentially large datasets makes them ideal for environments with extensive data logs, such as enterprise networks and cloud infrastructures. Third, as cyber threats become increasingly adaptive and stealthy, QML offers a dynamic solution that evolves alongside these threats by continuously learning from new patterns of attack. However, practical challenges remain, including the need for quantum hardware advancements, the development of hybrid quantum-classical models, and ensuring the interpretability of quantum models in audit scenarios. Despite these challenges, early research and experimental implementations demonstrate the potential of QML to revolutionize anomaly detection in cybersecurity audits. This paper concludes that while QML is still in its early stages, its application to anomaly detection holds promise for significantly enhancing the effectiveness of cyber security audits. The impact of this technology, when fully realized, could redefine how organizations protect their networks and data from ever-evolving cyber threats, making QML a critical area for future research and development in cybersecurity.
Classification is a fundamental aspect of leveraging big data for decision-making across domains such as engineering, medicine, economics, and beyond. This systematic review explores the application of quantum machine learning (QML) to address the classification challenges. Despite the rising popularity of QML, challenges persist regarding its practical efficacy and applicability relative to classical methods. This study seeks to classify and analyse the application of QML across diverse domains. Employing a systematic methodology, a total of 23 research articles were chosen after applying rigorous selection criteria between the years 2013 to 2023. At first, 520 research articles were identified, and narrowed down to 296 papers, the abstracts of the papers have been read, 61 full-text papers have been studied, and ultimately 23 papers have been selected for review. A methodological approach was employed, involving the selection of relevant papers from reputable databases, evaluation of these papers, and the subsequent derivation of insights into the performance of QML in practical classification applications. Findings indicate that QML classifiers mostly achieved superior performance compared to conventional methods across accuracy, high-dimensional data management, and computational efficiency. However, challenges remain, including the limitations of noisy quantum hardware and the requirement for hybrid and innovative quantum algorithms. Analysis of current trends reveals significant progress in hybrid quantum-classical computational methods. To conclude, this analysis reveals QML’s revolutionary capabilities while noting its current shortcomings. This study provides a thorough overview for researchers investigating QML’s application to real-world classification problems, and it offers guidelines for future research endeavors.