Quantum-Inspired Feature Engineering For Logistic Regression

Abstract

Banks predict credit default with Logistic Regression because regulators can read its coefficients—but it cannot express interactions. We add the missing non-linearity with a quantum-inspired feature map: 8 of the data's 23 columns become rotation angles of 8 qubits in an IQP circuit simulated in PennyLane, and 2 × 8 = 16 outputs are appended to the 23. On the UCI Default of Credit Card Clients data (30,000 clients, 5-fold CV) this lifts it from F1 = 0.462 to 0.517, while Kernel PCA, the strongest classical alternative at the same budget, reaches only 0.493; the gap survives FDR correction over 12 tests (p = 0.00007). The gain is specific to linear models: Random Forest, SVM, XGBoost, and k-NN, already non-linear, do not benefit. What caps n is the simulator, holding 2n amplitudes; on hardware the depth is independent of n.