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

arXiv · AI, language, vision and robotics · article · Sep 9, 2026 · UTC

Credit default prediction is a tabular classification problem in which modest gains in F1 translate directly into reduced financial exposure. We ask whether Instantaneous Quantum Polynomial-time (IQP) circuits can produce features that improve a classifier over both its raw classical baseline and Kernel PCA - the strongest unsupervised classical non-linear alternative - at an equal feature budget. The dataset provides 23 financial attributes per client; for an n-qubit circuit we select n of them, encode each as a rotation angle, and read 2n expectation values back out as new features. The moti

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