Concise Guide to Quantum Machine Learning

Rongji Li, Juan Xu, Jiabin Yuan +1 more

Yanche Ari Kustiawan, Khairil Imran Ghauth

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.

Sidney Shapiro

Hybrid quantum-classical machine learning represents a frontier in computational research, combining the potential advantages of quantum computing with established classical optimization techniques. PennyLane provides a Python framework that seamlessly bridges quantum circuits and classical machine learning, enabling researchers to build, optimize, and deploy variational quantum algorithms. This paper introduces PennyLane as a versatile tool for quantum machine learning, optimization, and quantum chemistry applications. We demonstrate use cases including quantum kernel methods, variational quantum eigensolvers, portfolio optimization, and integration with classical ML frameworks such as PyTorch, TensorFlow, and JAX. Through concrete Python examples with widely used libraries such as scikit-learn, pandas, and matplotlib, we show how PennyLane facilitates efficient quantum circuit construction, automatic differentiation, and hybrid optimization workflows. By situating PennyLane within the broader context of quantum computing and machine learning, we highlight its role as a methodological building block for quantum-enhanced data science. Our goal is to provide researchers and practitioners with a concise reference that bridges foundational quantum computing concepts and applied machine learning practice, making PennyLane a default citation for hybrid quantum-classical workflows in Python-based research.

Nikhil Gupta, Mr. VIGNESHWAR RANGINI, Mr. Bhushan +1 more

Aliasgar Abbasbhai Vohra, Rakesh Kumar S. Manjhi

Quantum Machine Learning integrates Quantum computing and Artificial Intelligence in such a way that it simplifies GST administration and compliance. Though QML is in early research stages, it is ready to address complex challenges in tax fraud detection, data analysis, and compliance monitoring. and revenue optimisation through its never-before-seen ability to do complex calculations. Quantum Machine Learning makes the GST administration and compliance much more concise and simpler. The Modern GST system faces challenges due to a lack of scale, complexity, and the risk of fraud in massive transactions. Quantum computing, along with advanced machine learning, aids in real-time analysis of a transaction, along with on-the-spot detection of fraud. This article reveals the revolution that would be brought by Quantum computing in GST, along with the limitations it possesses.

Yuxuan Du, Xinbiao Wang, Naixu Guo +6 more