arXiv subjects

Adam Innan

Publications and source records attributed to Adam Innan.

2 recordsLinked to original sources

arXiv2026

Q-SYNTH: Hybrid Quantum-Classical Adversarial Augmentation for Imbalanced Fraud Detection

Credit card fraud detection is fundamentally challenged by extreme class imbalance, where fraudulent transactions are rare yet operationally critical. This imbalance often biases supervised learners toward the legitimate class, leading to high overall accuracy but weaker fraud-class recall and F1-score. This paper introduces Q-SYNTH, a hybrid classical--quantum generative adversarial framework in which a parameterized quantum circuit serves as the generator and a classical neural network serves as the discriminator. Q-SYNTH is designed for minority-class fraud synthesis in tabular data and is evaluated along two dimensions: statistical fidelity to real fraud samples and downstream performance for fraud detection. To this end, generated samples are assessed using distributional similarity measures based on Kolmogorov-Smirnov statistics and Wasserstein distances, real-vs-synthetic detectability measured by AUC-ROC, and downstream classification performance across both quantum and classical classifiers. Under the reported protocol, Q-SYNTH reduces marginal distribution mismatch relative to a classical GAN baseline while maintaining competitive downstream fraud-detection performance. Although SMOTE achieves the strongest feature-wise similarity and the classical GAN attains the highest downstream performance in several settings, Q-SYNTH offers a favorable compromise between distributional fidelity and downstream performance, supporting the feasibility of hybrid quantum augmentation for imbalanced fraud detection.

cs.LG↗

arXiv2025

FiD-QAE: A Fidelity-Driven Quantum Autoencoder for Credit Card Fraud Detection

Credit card fraud detection is a critical task in financial security, as fraudulent transactions are rare, highly imbalanced, and often resemble legitimate ones. A wide range of classical machine learning methods, as well as more recent quantum machine learning approaches, have been investigated to address this challenge, each providing valuable progress but also leaving open questions regarding scalability, robustness, and adaptability to evolving fraud patterns. In this work, we introduce the Fidelity-based Quantum Autoencoder (FiD-QAE), a quantum architecture that employs fidelity estimation as the decision criterion for anomaly detection. Transactions are encoded into quantum states, compressed through a variational quantum circuit, and evaluated using the SWAP test to distinguish legitimate from fraudulent transactions. We conduct a comprehensive evaluation of FiD-QAE, including statistical analyses, multiple performance metrics, and robustness tests under quantum noise models. The results show that FiD-QAE maintains consistent performance across different imbalance levels and preserves robustness in noisy conditions. Moreover, validation on IBM Quantum hardware backends confirms the feasibility of our approach on real devices, with outcomes consistent with simulation. These findings position quantum fidelity as a powerful criterion for anomaly detection and highlight FiD-QAE as a promising direction that complements existing classical and quantum approaches, offering robustness and generalizability for financial fraud detection in realistic environments.

quant-ph↗