It is shown that ordinary hyperparameter choices move performance by considerably more than the quantum kernel does, that additional qubits degrade rather than improve performance through kernel concentration, and that the clustering framing itself fails at realistic class imbalance though kernel-based anomaly scoring does not.
Abstract
We benchmarked quantum kernel $k$-means against classical clustering for credit-card fraud detection on real transaction data, at up to \MaxQubits{} qubits, under a protocol with separated selection and reporting data and matched search budgets. We find no robust quantum advantage: the sign of the difference depends on register size, all effect sizes are below $0.013$ ARI, and the single significant advantage we observe is fully explained by the number of configurations searched. We further show that ordinary hyperparameter choices move performance by considerably more than the quantum kernel does, that additional qubits degrade rather than improve performance through kernel concentration, and that the clustering framing itself fails at realistic class imbalance though kernel-based anomaly scoring does not. We regard the methodological contribution as the more durable one. The search-budget ablation in particular is inexpensive and, in our case, decisive: it converted a statistically significant advantage into a procedural artefact. We would encourage its routine use.
Classical ML models for fraud detection tend to break down on rare events, which are the cases that matter most. In imbalanced transaction datasets, new fraud patterns often fall outside what these models were trained to recognize. This paper presents a Hybrid Quantum-Classical Anomaly Detection Framework built to address that gap. Particle Swarm Optimization (PSO) handles feature selection, after which transaction features are encoded into quantum-inspired Hilbert spaces using Angle and Amplitude Encoding. Two quantum models, a Quantum Kernel SVM (QK-SVM) and a Variational Quantum Circuit (VQC), process these representations, and their outputs are fused with scores from Random Forest, XGBoost, and One-Class SVM through a weighted scoring scheme. On the Credit Card Fraud Detection benchmark, the hybrid framework outperforms classical-only baselines on rare event detection across Precision, Recall, F1-Score, and ROC-AUC, without needing actual quantum hardware.
V. Maral, Aryan Dongare, Vidya Gandhale et al.· 2026 International Conferenc...· 0 citations
Credit card fraud detection remains a challenging binary classification problem because fraudulent transactions are rare, transaction patterns are complex, and false negatives may have important operational consequences. This study presents a Hybrid Classical–Quantum-Inspired Neural Network (HCQNN) with a simulated variational quantum circuit for credit card fraud detection. The proposed framework combines classical preprocessing, SMOTE-based class balancing, neural network-based feature learning, and quantum-inspired variational feature transformation. The model was evaluated using the Credit Card Fraud Detection dataset after applying SMOTE to the training data and was compared with three classical baseline classifiers: Logistic Regression, Decision Tree, and Linear Support Vector Machine. The experimental results show that the proposed HCQNN achieved an AUC of 97.85%, precision of 91.20%, recall of 90.10%, and an F1-score of 90.64%. These values indicate improved classification balance, particularly in the detection of minority-class fraud cases, compared with the selected baseline models. Training and validation behaviour also showed stable convergence, with training and validation accuracies exceeding 96% and 95%, respectively. Since the variational quantum circuit was simulated on classical hardware, the findings should be interpreted as evidence of the value of hybrid feature learning and quantum-inspired transformations rather than as proof of quantum computational advantage. The study provides a basis for further evaluation using broader datasets, additional baseline models, and real quantum hardware.
V. Srividhya, Srividhya Ganesan· Asian Journal of Research in...· 0 citations
Quantum kernel methods are a candidate approach for machine learning on near-term quantum hardware, but two practical problems limit their deployment: kernel values concentrate exponentially as the qubit count grows, and real-world datasets are often severely class-imbalanced. We present the first systematic empirical study of how these two effects interact. We define two diagnostic metrics, the Discrimination Ratio (DR) and the Quantum Imbalance Vulnerability Score (QIVS), which measure whether quantum kernels retain minority-class separability under concentration. Experiments span ten log-spaced imbalance ratios, five random seeds, five qubit counts (4 to 12), and five real-world datasets, and yield three results. First, DR stays above 1.0 at every qubit count tested (4 to 12), so the discriminative signal survives concentration. Second, at extreme imbalance (IR below 0.003), quantum kernel SVMs retain positive discriminative signal, crossing above DR=1.0 by IR≈0.0028, while the classical oversampling methods SMOTE and ADASYN produce zero minority-class recall throughout the same regime, a practical advantage for quantum kernels at the imbalance extreme. Third, QIVS follows a broadly monotonic decreasing trend, falling from 13.25 to 5.54 as the imbalance ratio increases, with a single minor fluctuation smaller than the cross-seed variability we measure elsewhere in the sweep. This trend makes QIVS a reliable diagnostic for practitioners choosing quantum kernels on imbalanced tasks.
Souvik Ghosh, Vijaysherly V.· 2026 International Conferenc...· 0 citations
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.
Taha M. Mahmoud, N. Kaabouch· 2026 6th International Confe...· 0 citations
Do quantum kernels improve cross-sectional stock return prediction? We run a controlled horse race on the Chinese A-share market in which a quantum fidelity kernel, a projected quantum kernel, and a classical RBF control share identical training subsamples, solver, and tuning budgets, so that only the kernel is exchanged. On the main evaluation -- a point-in-time universe and 170 walk-forward windows (2012-2025) -- no quantum advantage exists: the fidelity kernel is indistinguishable from its RBF control ($\Delta$IC $=+0.005$, $p=0.42$), and a $2\times2$ design crossing kernel type with training budget (a Nystrom extension to the full ~38,000-observation windows) shows quantum kernels matching, but never beating, equal-budget linear models; after family-wise correction no pairwise difference among eleven models is significant, with point estimates favoring penalized linear regressions throughout. We then document how the opposite conclusion arises: a 60-window evaluation on a universe screened with full-sample information makes the same quantum kernel appear dominant on stability criteria and significantly better than neural baselines. Interaction characteristics from the anomalies literature help nothing, quantum or classical; a widened bandwidth grid reveals an interior optimum rather than the near-classical endpoint a coarse grid suggests; and the geometric difference, while large throughout ($g \gg 1$), does not predict out-of-sample gains ($\rho=-0.20$). We propose protocol standards -- kernel-swap controls, budget-equalized comparisons, point-in-time universes, and multiplicity-robust inference -- for empirical claims of quantum advantage in finance.
The objective of this paper is to demonstrate that traditional statistical problems in credit scoring can be solved efficiently by implementing a quantum-enhanced version of the traditional Support Vector Machines algorithm. Three significant case studies are presented, involving regression, dichotomous and multi-class classification problems in the field of credit risk management. The analysis highlights the promise of the quantum kernel method as a competitive solution especially for multiclass problems with strongly structured features. Quantum-enhanced models tend to outperform classical approaches because they can capture more intricate non-linear relationships between variables, partly due to their ability to leverage entanglement. This advantage is particularly evident in credit risk assessment, where it can improve the credit evaluation process and help reduce credit losses for financial and insurance institutions.
Mattia Bacigalupo, P. Giribone· Risk Management Magazine· 0 citations