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Related Experiment Videos

HQRNN-FD: A Hybrid Quantum Recurrent Neural Network for Fraud Detection.

Yao-Chong Li1,2, Yi-Fan Zhang1,2, Rui-Qing Xu3

  • 1College of Information Engineering, Shanghai Maritime University, 1550 Haigang Avenue, Pudong New Area, Shanghai 201306, China.

Entropy (Basel, Switzerland)
|September 27, 2025
PubMed
Summary

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This summary is machine-generated.

This study introduces a hybrid quantum recurrent neural network for financial fraud detection. The novel approach significantly improves accuracy in detecting complex patterns, outperforming traditional methods.

Area of Science:

  • Artificial Intelligence
  • Quantum Computing
  • Financial Technology

Background:

  • Financial fraud detection is crucial for intelligent systems.
  • Deep learning struggles with high-dimensional, nonlinear features.
  • Class imbalance is a persistent challenge in fraud detection.

Purpose of the Study:

  • To introduce a novel hybrid quantum recurrent neural network for fraud detection (HQRNN-FD).
  • To enhance feature extraction and temporal dependency analysis for improved fraud detection.
  • To address class imbalance and improve model generalizability.

Main Methods:

  • Utilized variational quantum circuits (VQCs) with angle encoding, data reuploading, and hierarchical entanglement for quantum-enhanced feature extraction.
  • Integrated a recurrent neural network (RNN) with a self-attention mechanism for sequential analysis.
Keywords:
financial fraud detectionhybrid quantum–classical modelsquantum computingquantum financequantum machine learningvariational quantum circuits

Related Experiment Videos

  • Employed the synthetic minority over-sampling technique (SMOTE) to mitigate class imbalance.

    • Main Results:

    • Achieved an accuracy of 0.972 on public fraud detection datasets.
    • Outperformed conventional models by 2.4%.
    • Demonstrated robustness against quantum noise and scalability with increasing qubit numbers.

    Conclusions:

    • The hybrid quantum recurrent neural network (HQRNN-FD) is effective for imbalanced financial classification.
    • Quantum-enhanced feature extraction and temporal analysis improve fraud detection accuracy.
    • The model shows promise for real-world financial fraud detection applications.