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

• URL: http://arxiv.org/abs/2605.21164v1
• Date: Wed, 20 May 2026 13:32:18 GMT
• Title: Q-SYNTH: Hybrid Quantum-Classical Adversarial Augmentation for Imbalanced Fraud Detection
• Abstract summary: Credit card fraud detection is fundamentally challenged by extreme class imbalance.<n>This paper introduces Q-SYNTH, a hybrid classical--quantum generative adversarial framework.
• Score: 2.063079740825497
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• How Quantum Is the Advantage? A Fair, Calibration- and Noise-Aware Benchmark and Attribution Audit of Quantum Machine Learning for Network Intrusion Detection [0.0]
  Quantum machine learning for network intrusion detection is routinely reported to reach near-perfect accuracy.<n>Most rigorous studies find that well-tuned classical models remain competitive.<n>We present a unified, reproducible QML-IDS benchmark evaluating hybrid variational quantum circuits.
  arXiv  Detail & Related papers  (2026-08-13T07:44:35Z)
• QC-SMOTE: Quality-Controlled SMOTE for Imbalanced Classification [11.542407696902115]
  QC-SMOTE estimates minority sample reliability using a composite neighbourhood trustworthiness score.<n>Synthetic candidates are generated using an IPQ-guided best-of-K strategy.
  arXiv  Detail & Related papers  (2026-06-23T14:23:18Z)
• Hybrid Quantum-Classical GANs for the Generation of Adversarial Network Flows [0.0]
  We propose a hybrid quantum-classical GAN (QC-GAN) framework to generate synthetic network traffic flows mimicking malicious traffic.<n>A classical discriminator will be trained on real-world datasets and the proposed QC-GAN-generated fake network flows.<n>In our attack model, we assume that the attacker is a state actor with access to limited quantum computing power, whereas the discriminator is chosen to be classical.
  arXiv  Detail & Related papers  (2026-05-07T17:43:25Z)
• Hybrid LSTM-Graph Neural Framework for Robust Financial Fraud Detection and Adversarial Resilience [0.0]
  This paper proposes FraudShield AI, a hybrid framework that integrates Long Short-Term Memory (LSTM) networks with hand-crafted Graph Topological Features.<n>A Focal Loss objective is used to address class imbalance, and a dynamic thresholding mechanism is introduced to improve resilience against low-value smurfing attacks.
  arXiv  Detail & Related papers  (2026-05-02T02:10:20Z)
• Domain-Aware Hybrid Quantum Learning via Correlation-Guided Circuit Design for Crime Pattern Analytics [54.3410887955176]
  Crime pattern analysis is critical for law enforcement and predictive policing.<n>The surge in criminal activities from rapid urbanization creates high-dimensional, imbalanced datasets.<n>This study presents a quantum-classical comparison framework for crime analytics.
  arXiv  Detail & Related papers  (2026-04-08T06:05:25Z)
• Continual Quantum Architecture Search with Tensor-Train Encoding: Theory and Applications to Signal Processing [68.35481158940401]
  CL-QAS is a continual quantum architecture search framework.<n>It mitigates challenges of costly encoding amplitude and forgetting in variational quantum circuits.<n>It achieves controllable robustness expressivity, sample-efficient generalization, and smooth convergence without barren plateaus.
  arXiv  Detail & Related papers  (2026-01-10T02:36:03Z)
• Quantum Machine Learning Approaches for Coordinated Stealth Attack Detection in Distributed Generation Systems [0.0]
  Coordinated stealth attacks are a serious cybersecurity threat to distributed generation systems.<n>This study investigates quantum machine learning approaches for detecting coordinated stealth attacks on a distributed generation unit in a microgrid.
  arXiv  Detail & Related papers  (2025-12-30T18:32:13Z)
• Quantum Gated Recurrent GAN with Gaussian Uncertainty for Network Anomaly Detection [10.435718832887988]
  Anomaly detection in time-series data is a critical challenge with significant implications for network security.<n>Recent quantum machine learning approaches have shown promise in capturing complex data distributions for anomaly detection but remain constrained by limited qubit counts.<n>We introduce in this work a novel Quantum Gated Recurrent Unit (QGRU)-based Generative Adversarial Network (GAN) for robust network anomaly detection.
  arXiv  Detail & Related papers  (2025-10-30T13:39:44Z)
• Hybrid Quantum-Classical Neural Networks for Few-Shot Credit Risk Assessment [52.05742536403784]
  This work tackles the challenge of few-shot credit risk assessment.<n>We design and implement a novel hybrid quantum-classical workflow.<n>A Quantum Neural Network (QNN) was trained via the parameter-shift rule.<n>On a real-world credit dataset of 279 samples, our QNN achieved a robust average AUC of 0.852 +/- 0.027 in simulations and yielded an impressive AUC of 0.88 in the hardware experiment.
  arXiv  Detail & Related papers  (2025-09-17T08:36:05Z)
• Shift Before You Learn: Enabling Low-Rank Representations in Reinforcement Learning [56.87989363424]
  We show that a low-rank structure naturally emerges in the shifted successor measure.<n>We quantify the amount of shift needed for effective low-rank approximation and estimation.
  arXiv  Detail & Related papers  (2025-09-05T15:48:20Z)
• Toward Practical Quantum Machine Learning: A Novel Hybrid Quantum LSTM for Fraud Detection [0.1398098625978622]
  We present a novel hybrid quantum-classical neural network architecture for fraud detection.<n>By leveraging quantum phenomena such as superposition and entanglement, our model enhances the feature representation of sequential transaction data.<n>Results demonstrate competitive improvements in accuracy, precision, recall, and F1 score relative to a conventional LSTM baseline.
  arXiv  Detail & Related papers  (2025-04-30T19:09:12Z)
• Problem-Dependent Power of Quantum Neural Networks on Multi-Class Classification [83.20479832949069]
  Quantum neural networks (QNNs) have become an important tool for understanding the physical world, but their advantages and limitations are not fully understood. Here we investigate the problem-dependent power of QCs on multi-class classification tasks. Our work sheds light on the problem-dependent power of QNNs and offers a practical tool for evaluating their potential merit.
  arXiv  Detail & Related papers  (2022-12-29T10:46:40Z)
• Robustness Certificates for Implicit Neural Networks: A Mixed Monotone Contractive Approach [60.67748036747221]
  Implicit neural networks offer competitive performance and reduced memory consumption. They can remain brittle with respect to input adversarial perturbations. This paper proposes a theoretical and computational framework for robustness verification of implicit neural networks.
  arXiv  Detail & Related papers  (2021-12-10T03:08:55Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.