A Mixture-of-Experts Framework for Practical Hybrid-Quantum Models in Credit Card Fraud Detection

2026

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This paper introduces a hybrid quantum-classical framework that enhances credit card fraud detection by strategically leveraging quantum computing, offering practical improvements with minimal latency impact.

* Explores a novel Mixture-of-Experts (MoE) framework integrating a Guided Quantum Compressor architecture with a state-of-the-art gradient-boosted tree classifier.
* Achieves improved average precision scores compared to XGBoost on a European credit card dataset with severe class imbalance.
* Demonstrates that the hybrid approach enhances fraud detection performance while adding only a minimal increase in inference time, making it suitable for real-world financial applications.

Tags: quantum computing, fraud detection, machine learning, hybrid models, mixture-of-experts

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