FT-FM: A Financial-Transaction-Specific Quantum Feature Map for Variational Quantum Classifiers in Fraud Detection
Abstract
Preliminary empirical characterisation on synthetic financial-transaction data confirms FT-FM's predicted expressibility advantage over generic feature maps (Kullback–Leibler divergence to the Haar distribution of 4.01 ± 1.16 versus 21.24 ± 0.79 for a ZZ-like baseline and 19.53 ± 0.43 for an amplitude-like baseline at matched qubit count), and exposes a kernel-concentration phenomenon (Meyer–Wallach Q = 0.948 ± 0.004) that motivates a refined construction (FT-FM-lite) with a tunable entanglement-strength hyperparameter; full empirical benchmarking on Kaggle CCF, CICIDS-2017, and UNSW-NB15 is the subject of a forthcoming paper in this series.
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