This study proposes a quantum-kernel framework based on a Quantum Support Vector Machine (QSVM) with a novel XYZ feature map integrating RY and RZ rotations for encoding, full all-to-all CX entanglement, and an additional RX rotation layer to obtain higher-order information. The performance of the proposed method is evaluated using a publicly available dataset and benchmarked this embedding against standard Z, ZZ, and Pauli feature maps under matched circuit depth and resource budgets. Across settings, the suggested XYZ map yields the highest performance with accuracy of 93.2%, precision of 0.934, recall of 0.925 and G-mean of 0.93. These results indicate that fully entangling quantum embeddings can enhance the approximation capacity of quantum-kernel for real-world anomaly detection and provide a path toward quantum-assisted fraud analytics.
This study proposes a quantum-kernel framework based on a Quantum Support Vector Machine (QSVM) with a novel XYZ feature map integrating RY and RZ rotations for encoding, full all-to-all CX entanglement, and an additional RX rotation layer to obtain higher-order information. The performance of the proposed method is evaluated using a publicly available dataset and benchmarked this embedding against standard Z, ZZ, and Pauli feature maps under matched circuit depth and resource budgets. Across settings, the suggested XYZ map yields the highest performance with accuracy of 93.2%, precision of 0.934, recall of 0.925 and G-mean of 0.93. These results indicate that fully entangling quantum embeddings can enhance the approximation capacity of quantum-kernel for real-world anomaly detection and provide a path toward quantum-assisted fraud analytics.
Innovative methods for information security and fraud prevention are required in today's digital environment due to the expanding volume of data and the increasing complexity of cyber threats. The use of quantum computing techniques to improve fraud detection and classification systems is investigated in this study. The study's machine learning framework integrates three distinct quantum algorithms to improve classification techniques. The first technique uses a Pauli feature map and a Quantum Support Vector Classifier (QSVC) that leverages a quantum kernel to transform classical input into quantum states. The second technique use a ZZ feature map with "linear" entanglement and a support vector classifier model, utilizing quantum kernels to enhance quantum systems. The third method utilizes Variational Quantum Circuits (VQC) with actual amplitudes, which integrate quantum and conventional machine learning techniques to provide optimized classification. The best results were obtained by the QSVC using ZZ feature maps and linear entanglement, which had a precision of 1.0 and a notable decrease in false positives. In order to improve fraud detection systems' accuracy and dependability and offer strong solutions to financial institutions, this study shows how quantum computing has the potential to completely transform cybersecurity.
Abstract The exponential growth of digital financial transactions has increased the demand for intelligent and accurate fraud detection systems. This study proposes a Quantum-Enhanced Fraud Detection Framework that integrates classical feature engineering with advanced quantum machine learning techniques. The framework begins with comprehensive data preprocessing, including Mean, Median, and Mode Imputation to handle missing values and Z-Score Standardization along with Min–Max Scaling to normalize the data. The processed features are then transformed into quantum states using the ZZFeatureMap, enabling the representation of complex, non-linear correlations through quantum entanglement and superposition. The Quantum Kernel evaluates the pairwise similarity of quantum-encoded samples, capturing intricate relationships in high-dimensional feature spaces. Finally, a Quantum Support Vector Machine (QSVM) classifier is trained using the quantum kernel matrix to differentiate between fraudulent and legitimate transactions. The proposed method achieved an accuracy of 91%, demonstrating superior performance over conventional approaches by efficiently identifying subtle fraud patterns. The results validate the potential of Quantum Machine Learning (QML) as a powerful paradigm for financial fraud detection, combining classical data preprocessing with quantum computational advantages to achieve higher generalization and robustness.
Detecting credit card fraud is notoriously difficult because fraudulent charges make up less than 0.2% of total transactions. Quantum Machine Learning (QML), particularly Quantum Kernel Support Vector Machines (QSVM), promises richer feature representations by embedding classical data into high-dimensional Hilbert spaces. However, whether this theoretical advantage holds up when managing real-world financial data bottlenecks remains unclear. We designed a controlled benchmark comparing three quantum encodings, Angle, ZZFeatureMap, and IQP against classical models while keeping sample sizes, feature counts, and scaling strictly matched. To handle class skew without corrupting quantum state overlaps, we embedded synthetic minority oversampling (SMOTE) directly into the quantum Gram matrix workflow, raising PR-AUC scores by roughly +0.04 across feature maps. When trained on the full European credit card dataset with 30 features, classical baselines reached a top PR-AUC of 0.983. However, restricting classical models to match QSVM hardware constraints (500 samples, 4 PCA features) dropped their PR-AUC to 0.750. This puts our best quantum result 0.730 PR-AUC using IQP encoding on 150 samples into realistic context. Testing on physical quantum hardware reduced performance further to 0.62 due to CNOT gate errors and thermal noise. Overall, our findings pinpoint two main operational bottlenecks for near-term quantum kernels: the scaling of Gram matrix evaluation and heavy information loss from squeezing features onto small qubit registers.
Quantum algorithms have demonstrated extraordinary potential in numerous fields. The demand for fraud detection in credit cards is growing increasingly, which calls for more efficient algorithms. This work adopts the detection method of Quantum Support Vector Machine (QSVM) to improve the accuracy of credit card fraud detection. Meanwhile, Quantum-Synthetic Minority Oversampling Technique (Quantum-SMOTE) is utilized to address the issue of imbalanced credit card datasets. The QSVM model maps classical data to a high-dimensional Hilbert space through quantum feature mapping, thereby enhancing the separability of features. Then, quantum kernel computation is performed to calculate the inner products between features for model training. The research results show that the QSVM achieves the best overall performance, which indicates that QSVM has significant potential in handling credit card fraud detection.
This work presents a quantum machine learning approach for bipartite entanglement detection in 3×3 quantum systems using a support vector machine with quantum kernels. The classifier is trained to distinguish separable from entangled states, which includes both PPT-detectable and PPT-non-detectable instances, based on classical simulations of parameterized quantum feature maps combined with dimensionality reduction via PCA. Our results show that Pauli-based feature map kernels achieve higher classification accuracy than amplitude embedding at the same PCA dimension, reaching up to 78% accuracy for PCA =8, while amplitude embedding requires PCA =32 to attain comparable performance. We also show that increasing the number of Pauli feature map repetitions does not lead to consistent accuracy improvements, despite significantly increasing circuit depth and computational overhead.
The support vector machine algorithm with a quantum kernel estimator (QSVM-Kernel), as a leading example of a quantum machine learning technique, has undergone significant advancements. Nevertheless, its integration with classical data presents unique challenges. While quantum computers primarily interact with data in quantum states, embedding classical data into quantum states using feature mapping techniques is essential for leveraging quantum algorithms Despite the recognized importance of feature mapping, its specific impact on data classification outcomes remains largely unexplored. This study addresses this gap by comprehensively assessing the effects of various feature mapping methods on classification results, taking medical data analysis as a case study. In this study, the QSVM-Kernel method was applied to classification problems in two different and publicly available medical datasets, namely, the Wisconsin Breast Cancer (original) and The Cancer Genome Atlas (TCGA) Glioma datasets. In the QSVM-Kernel algorithm, quantum kernel matrices obtained from 9 different quantum feature maps were used. Thus, the effects of these quantum feature maps on the classification results of the QSVM-Kernel algorithm were examined in terms of both classifier performance and total execution time. As a result, in the Wisconsin Breast Cancer (original) and TCGA Glioma datasets, when Rx and Ry rotational gates were used, respectively, as feature maps in the QSVM-Kernel algorithm, the best classification performances were achieved both in terms of classification performance and total execution time. The contributions of this study are that (1) it highlights the significant impact of feature mapping techniques on medical data classification outcomes using the QSVM-Kernel algorithm, and (2) it also guides undertaking research for improved QSVM classification performance.