Jul 2026· 2026 International Conference on Intelligent and Sustainable AI Systems (ICOSAAS)· pp. 1-7· 0 citations· 18 references
Abstract
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.
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
Stock market crashes pose a significant risk to investors and regulators; they are difficult to predict in their early stages because of market volatility. The conventional machine learning methods often struggle to learn financial time series having complex structures such as non-linearity, high dimensionality, and dynamic volatility, often found in a real-life turbulent market. Although gold and silver have been considered relatively stable investment assets, the crashing of these metal rates impacts retail investors as well as global finance in a powerful manner. To overcome these limitations, the current work presents a hybrid quantum-classical Machine Learning (QCML) approach for forecasting commodity market crashes that combines Quantum Support Vector Machines (QSVM) with eXtreme Gradient Boosting (XGBoost). In order to spot crash events, we have proposed a new drawdown-based labeling approach based on significant price declines as well as short-term recovery trends. Feature engineering and training is performed on baselines such as volatility measures, momentum indicators, and trend-based ratios. The proposed design also features Random Forest thresholding, ensemble weight optimization, and feature selection. As practical quantum hardware with sufficient capability is currently unavailable for our limited experiment, the quantum component is implemented using a simulator. The proposed Hybrid QSVM–XGBoost framework achieved its best performance for the Gold 3-month prediction horizon with an ROC-AUC of 0.81. The silver market, in contrast, has lower predictability (ROC-AUC = 0.62) because of greater volatility and complex dynamics. Our results demonstrate the feasibility of integrating simulated quantum-kernel learning with classical machine learning techniques for commodity market crash prediction. While the proposed framework shows promising predictive performance, it does not claim quantum advantage or quantum supremacy under current NISQ-era hardware limitations.
R. Singh, S. Muhuri, Yadwinder Singh· Scientific Reports· 0 citations
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.
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.
Sai Sakunthala Guddanti, Anupama Ray, Mrunal Arun Kumavat et al.· 0 citations
We developed a novel workflow that leverages Quantum Kernel feature space expressiveness combined with classical dimensionality reduction techniques. This workflow enables detection and visual identification of multiple simultaneous anomalies using acoustic data from a single non-contact sensor. Our newly developed method provides an intuitive interface for operators to identify specific anomalies. By combining conventional Mel-frequency cepstral coefficients (MFCC) with principal component analysis (PCA), the newly constructed Quantum Kernel achieves near-perfect classification (F1 = 1.0 under specific conditions: j ≥ 9 features, file-level data splits) of complex multi-source anomalies, significantly outperforming the classical RBF kernel (F1 ≈ 0.75-0.76) and 1D-CNN autoencoder baseline (F1 ≈ 0.60-0.85). The Quantum Kernel's superior representational power enables accurate anomaly detection with minimal training data. Our results demonstrate simulation-based evidence of potential quantum advantage under ideal conditions, where the performance of our method dramatically exceeds that of classical approaches as the number of features increases, pending validation on noisy intermediate-scale quantum (NISQ) hardware. This hybrid quantum-classical machine learning approach demonstrates significant potential for industrial applications, particularly for complex time-series data in data-scarce regimes where classical methods exhibit limited performance.
In this paper, a forensic end-to-end machine learning pipeline to detect financial fraud in PaySim synthetic mobile money dataset a corpus of 6,362,620 transactions with an extreme class imbalance ratio of 0.129% is presented. Proposed system is based on the distributed MLlib framework of Apache Spark in order to overcome the scalability issues associated with processing millions of financial records. Multicollinear raw ledger features are substituted with seven theoretically-based feature engineering transformations such as accounting discrepancy indices and account drain ratios as well as high-risk categorical flags. Ten classifiers such as Logistic Regression, Decision Tree variants, Random Forest ensembles, Gradient Boosted Trees, Linear SVC and a Factorization Machine are considered in a forensic model tournament. The imbalance between classes is alleviated through random undersampling to a balanced 50:50 training sample. The Matthews Correlation Coefficient (MCC) is the statistically rigorous evaluation index of the imbalanced binary classification that allows model selection. The winning ensemble model has a Recall of 99.39%, a Precision of 100.00%, an MCC of 0.9941 and a Financial Protection Index of over 96% which shows the operational reliability and good business value in preserving capital. Limitations regarding the synthetic nature of the data, the absence of sequence modeling, the schema dependence of the engineered features and the batch nature of the deployment are also discussed.
Anuran Baruah, Sahithya Sambhrama, K. S· 2026 4th International Confe...· 0 citations