Fidelity-driven Quantum Autoencoder Achieves Robust Fraud Detection for Imbalanced Financial Data
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Credit card fraud represents a significant and evolving threat to financial security, demanding increasingly sophisticated detection methods.
Mansour El Alami, Adam Innan, and Nouhaila Innan, alongside colleagues from Hassan II University of Casablanca and New York University Abu Dhabi, present a novel approach to this challenge with the development of FiD-QAE, a Fidelity-Driven Quantum Autoencoder. This architecture leverages the principles of quantum computing to encode transactions and identify anomalies, employing fidelity estimation as the key decision criterion for distinguishing fraudulent activity from legitimate transactions.
The team demonstrates that FiD-QAE not only achieves consistent performance across varying data imbalances, but also maintains robustness even in the presence of noise, and crucially, validates the feasibility of this quantum approach on actual hardware, offering a promising new direction for fraud detection in complex financial systems.
Quantum Machine Learning for Fraud Detection Research into credit card fraud detection is increasingly focused on the potential of quantum machine learning (QML), exploring various quantum models, including quantum neural networks, support vector machines, and generative adversarial networks, to improve accuracy and efficiency. Most studies adopt hybrid approaches, combining the strengths of classical machine learning with quantum computation to leverage quantum algorithms for specific tasks like feature selection and model training. A growing area of investigation involves federated learning, which enables privacy-preserving fraud detection by training models on decentralized data sources without sharing sensitive raw data; quantum federated learning is also being explored. Recognizing the difficulty of obtaining real-world fraud data, researchers frequently utilize synthetic datasets to test and benchmark their models. Hybrid quantum-classical models are gaining traction, integrating quantum feature selection with classical machine learning. Furthermore, research into privacy-preserving federated frameworks with hybrid quantum-enhanced learning is expanding the possibilities for secure fraud detection.
The team encoded transaction data into quantum states, effectively representing financial information as quantum bits, before compressing this data using a carefully engineered variational quantum circuit. This circuit efficiently reduces data dimensionality while preserving critical features, forming the core of the autoencoder. The compressed quantum states then undergo evaluation using the SWAP test, a quantum algorithm that precisely measures the similarity between the reconstructed and original states, serving as the primary criterion for anomaly detection. The system consistently maintained performance even when the ratio of fraudulent to legitimate transactions shifted, demonstrating its reliability in realistic scenarios. Researchers also subjected the system to tests under simulated quantum noise, mimicking the imperfections inherent in real quantum hardware, and confirmed that FiD-QAE preserved its accuracy even in noisy conditions. Validating the feasibility of their approach, the team implemented and tested FiD-QAE on IBM Quantum hardware backends, confirming that the outcomes obtained on actual quantum devices aligned with those predicted by simulations. This work addresses the critical challenge of identifying rare and imbalanced fraudulent transactions, which often closely resemble legitimate ones.
The team designed FiD-QAE to employ fidelity estimation as the primary decision criterion for anomaly detection, encoding transactions into quantum states and compressing them using a variational quantum circuit. A key innovation is the use of the SWAP test to distinguish between legitimate and fraudulent transactions, offering a novel approach to fraud identification. Experiments demonstrate that FiD-QAE maintains consistent performance across varying levels of data imbalance, a common issue in fraud detection. The system achieves approximately 92% accuracy and 90% precision in identifying fraudulent transactions, representing a significant advancement over existing quantum autoencoders. Notably, the research confirms the robustness of FiD-QAE under quantum noise, a critical factor for real-world implementation. Validation on IBM Quantum hardware backends confirms the feasibility of the approach, with outcomes consistent with simulation results.
The team successfully implemented FiD-QAE using only 4 qubits, demonstrating an efficient design for practical application.
The team successfully encoded transaction data into quantum states, compressed this information, and then utilized quantum fidelity, a measure of state similarity, as the primary criterion for identifying fraudulent activity. Comprehensive evaluation, including statistical analyses and multiple performance metrics, demonstrates the model’s robustness and ability to maintain reliable performance even with imbalanced datasets and simulated quantum noise. The results show that this approach achieves a strong balance between precision and recall, minimizing false positives while effectively identifying fraudulent transactions. Importantly, the model exhibits improved discriminative capability compared to existing methods while requiring fewer quantum resources, suggesting potential for practical implementation. Validation on hardware backends confirms the feasibility of the approach and consistency with simulation results, emphasizing the potential of quantum models for tackling complex financial security challenges. Future work will focus on advancing quantum autoencoder architectures and exploring their implementation on noisy intermediate-scale quantum hardware, with the goal of improving both the reliability and scalability of financial security systems.
This research highlights the strategic role quantum computation can play in addressing imbalanced classification tasks, such as credit card fraud detection, and opens promising directions for future development in this field. 👉 More information 🗞 FiD-QAE: A Fidelity-Driven Quantum Autoencoder for Credit Card Fraud Detection 🧠 ArXiv: https://arxiv.org/abs/2512.12689 Tags: