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Q-SYNTH demonstrates a 35% improvement in Kolmogorov-Smirnov statistics over classical GANs by using a hybrid quantum-classical architecture to better model rare fraud patterns. This approach effectively mitigates mode collapse and distribution mismatch, offering a viable pathway to enhance detection recall for imbalanced datasets where conventional methods struggle. The results suggest that integrating quantum circuits with classical discriminators provides a distinct advantage for augmenting tabular data in high-stakes financial security applications.

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Quantum Circuits Synthesise Realistic Fraud Data to Beat Detection Bias

Quantum ZeitgeistMay 23, 20266 min read

Despite achieving competitive fraud detection, current generative models often struggle to realistically replicate rare transaction patterns. Now, a new hybrid quantum-classical system, Q-SYNTH, demonstrably reduces the statistical distance between synthetic and genuine fraudulent data compared to conventional methods. While not universally superior, this approach offers a novel pathway towards bolstering financial security in an era of increasingly sophisticated fraud.

Adam Innan and colleagues at Hassan II University of Casablanca, in collaboration with NYUAD Research Institute and New York University, have developed Q-SYNTH, a new hybrid quantum-classical framework for generating synthetic fraudulent data and improving detection rates. The framework addresses the rarity of fraudulent transactions, a problem that biases conventional machine learning algorithms. Q-SYNTH explores the potential of combining quantum computing with classical techniques to address challenges posed by imbalanced datasets, exhibiting a favourable balance between statistical similarity to real fraud and overall detection performance. It utilises a quantum circuit as a generator and a classical neural network as a discriminator, offering a flexible avenue for augmenting tabular data and enhancing fraud-class recall…

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