Quantum Feature Engineering for Credit Default Prediction: When and Why IQP Circuits Help Linear Classifiers

Exploring practical bounds for quantum machine learning on tabular data, researchers tested Instantaneous Quantum Polynomial-time (IQP) circuits as feature extractors for credit default prediction. On the UCI credit card dataset, appending 16 IQP features from an 8-qubit circuit lifted Logistic Regression's F1 score from 0.462 to 0.517, outperforming Kernel PCA at an identical feature budget. Crucially, non-linear models like XGBoost and Random Forest saw zero improvement. The experiment demonstrates that the quantum circuit does not fabricate signal, but instead provides an explicit non-linear projection that compensates specifically for the architectural limits of linear classifiers.