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

• URL: http://arxiv.org/abs/2505.00137v1
• Date: Wed, 30 Apr 2025 19:09:12 GMT
• Title: Toward Practical Quantum Machine Learning: A Novel Hybrid Quantum LSTM for Fraud Detection
• Authors: Rushikesh Ubale, Sujan K. K., Sangram Deshpande, Gregory T. Byrd,
• Abstract summary: 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.
• Score: 0.1398098625978622
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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

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