TL;DR
This study systematically evaluates the efficacy of two quantum machine learning algorithms-Quantum Support Vector Machine (QSVM) and Quantum Neural Networks (QNN) on IBM quantum simulation platforms to provide a comprehensive assessment of QSVM and QNN under current Noisy Intermediate-Scale Quantum (NISQ) constraints.
Quantum machine learning (QML) faces practical limitations due to noisy intermediate-scale quantum (NISQ) constraints, including noise, restricted qubit availability, and unstable optimization. This paper proposes HyQNet, a resource-aware hybrid quantum–classical framework designed to address these challenges through efficient circuit execution and adaptive optimization. The framework integrates optimized quantum circuits with classical learning strategies to improve scalability and stability under NISQ conditions. Experimental results on Iris, Wine, and Breast Cancer datasets show that HyQNet achieves an accuracy of 95.1% and F1-score of 94.8%, outperforming variational QNN (92.6%) and quantum SVM (91.2%). It also reduces runtime to 16.9 s compared to 20.5 s for VQNN, while maintaining efficient utilization of 8 qubits. Statistical analysis confirms significance (p < 0.05), and ablation studies validate the contribution of each component. The results demonstrate improved convergence stability and resource efficiency in hybrid quantum learning systems.
It is demonstrated that certain QML algorithms exhibit remarkable resilience on the IBM Heron quantum processor, approaching simulator-level performance on devices with high quantum volume, which underscores the promise of QML despite hardware limitations.
The way in which classical data are encoded into quantum states plays a significant role in both classification performance and quantum circuit complexity in Quantum Machine Learning. In this study, the effects of different quantum feature encoding strategies on Quantum Support Vector Machine performance were investigated using five binary classification datasets. In particular, the statistical relationships between features were incorporated into quantum circuits through \(RY(\theta)\) and controlled-\(RY(\theta)\) gates, and this approach was compared with conventional quantum feature maps. The results demonstrate that incorporating statistical relationships into the encoding process can influence classification performance. However, more complex and densely entangled circuits do not necessarily yield higher performance. In addition, a composite evaluation metric was employed to jointly assess predictive performance, generalization, and circuit cost. The findings across the five datasets indicate that the choice of quantum feature encoding strategy should account for the underlying structure of the data and that predictive performance should be evaluated together with quantum circuit complexity.
Over The past decade, quantum computing and machine learning have each witnessed tremendous growth in their respective fields. While quantum computing promises a completely novel paradigm for computation, based on the principles of superposition, entanglement, and interference, machine learning has firmly cemented its place in the sphere of data-driven decision-making. This paper seeks to provide a practical solution to this question, implementing a quantum classical learning system that is capable of performing binary classification tasks. We achieve this through the creation of a Quantum Kernel Support Vector Classifier (QSVC) using the Qiskit framework. While the classical feature set from the Iris dataset is scaled appropriately for the quantum system, the feature set is then represented in the quantum state through the ZZFeatureMap circuit. The QSVC utilizes the kernel-based similarity between quantum states. The results were promising, with the QSVC obtaining a classification accuracy of 92% over a test set, with the model trained in 6.32 seconds on a simulated quantum backend. The decision boundaries learned by the model were complex, and the quantum kernel representation of the feature set was able to capture the underlying class structure, which would have been difficult to achieve in a classical low-dimensional feature space. Our method, with quantum circuits relegated to state preparation and measurement while leaving the optimization to classical processes, is designed to operate within these limits. This makes our method not only theoretically interesting but also immediately applicable to existing simulators and quantum hardware.
This paper presents a comparative benchmarking study of classical and quantum machine learning models for intrusion detection using three benchmark datasets: NSL-KDD, UNSW-NB15, and MQTTEEB-D2025. The study evaluates how preprocessing choices, feature selection strategies, and quantum encoding methods influence model performance across datasets with different levels of noise and complexity. A unified pipeline is adopted, incorporating normalization, imbalance handling, dimensionality reduction, and two feature selection approaches: Random Forest importance and a quantum-aware method based on Quantum Kernel Alignment with Mutual Information. Four models are assessed: Support Vector Machine, Random Forest, Quantum Support Vector Machine, and Pegasos Quantum SVM. Results show that classical models remain stable across datasets, while quantum models are more sensitive to feature representation and kernel alignment. Quantum performance improves significantly with quantum-aware feature selection, particularly on cleaner datasets, whereas heterogeneous datasets remain challenging. Pegasos Quantum SVM offers a favorable balance between accuracy and computational efficiency, highlighting the importance of preprocessing alignment for practical quantum intrusion detection.
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.
