Toward Practical Quantum Machine Learning: A Novel Hybrid Quantum LSTM for Fraud Detection

We present a novel hybrid quantum-classical neural network architecture for fraud detection that integrates a classical Long Short-Term Memory (LSTM) network with a variational quantum circuit. By leveraging quantum phenomena such as superposition and entanglement, our model enhances the feature representation of sequential transaction data, capturing complex non-linear patterns that are challenging for purely classical models. A comprehensive data preprocessing pipeline is employed to clean, encode, balance, and normalize a credit card fraud dataset, ensuring a fair comparison with baseline models. Notably, our hybrid approach achieves per-epoch training times in the range of 45-65 seconds, which is significantly faster than similar architectures reported in the literature, where training typically requires several minutes per epoch. Both classical and quantum gradients are jointly optimized via a unified backpropagation procedure employing the parameter-shift rule for the quantum parameters. Experimental evaluations demonstrate competitive improvements in accuracy, precision, recall, and F1 score relative to a conventional LSTM baseline. These results underscore the promise of hybrid quantum-classical techniques in advancing the efficiency and performance of fraud detection systems.Keywords: Hybrid Quantum-Classical Neural Networks, Quantum Computing, Fraud Detection, Hybrid Quantum LSTM, Variational Quantum Circuit, Parameter-Shift Rule, Financial Risk Analysis

@article{ubale2025_2505.00137,
  title={ Toward Practical Quantum Machine Learning: A Novel Hybrid Quantum LSTM for Fraud Detection },
  author={ Rushikesh Ubale and Sujan K.K. and Sangram Deshpande and Gregory T. Byrd },
  journal={arXiv preprint arXiv:2505.00137},
  year={ 2025 }
}