FT-FM-lite: Tunable-Entanglement Refinement and Empirical Benchmarking on Financial-Cybersecurity Datasets
Abstract
Abstract—The financial-transaction feature map FT-FM, introduced
in the first paper of this series, is empirically more
expressive than generic quantum encodings but operates in
a near-maximally entangled regime in which quantum-kernel
values concentrate and kernel-based classification fails. This
paper introduces FT-FM-lite, a refinement of FT-FM with a
single entanglement-strength hyperparameter β ∈ [0, 1] that
preserves the domain-informed encoding axioms while allowing
the feature map to operate in the intermediate expressibility–
trainability regime; β = 1 recovers FT-FM exactly. A validationbased
protocol selects β per training split. In a ten-seed synthetic
diagnostic against exactly implemented matched controls,
validation-selected FT-FM-lite attains a mean held-out F1 of 0.620
(95% bootstrap CI [0.530, 0.709]), exceeding exact amplitude
encoding (0.362) and Qiskit’s ZZFeatureMap (0.033) with Holm–
Bonferroni-corrected significance, while its advantage over fullstrength
FT-FM (0.490) is not significant after correction. We
further specify a leakage-controlled, case-control benchmarking
protocol with paired Wilcoxon significance testing and apply it
to the Kaggle Credit-Card Fraud dataset over ten repeated stratified
case-control splits. In contrast to the synthetic diagnostic,
validation selection chose full strength (β = 1) in every seed, and
FT-FM-lite attained a mean case-control test F1 of 0.891 (95%
bootstrap CI [0.870, 0.907]; ROC-AUC 0.971; PR-AUC 0.976)
— significantly above exact amplitude encoding and significantly
below a matched RBF-SVM after Holm–Bonferroni correction,
and statistically indistinguishable from Qiskit’s ZZFeatureMap
and the remaining classical baselines. Balanced-cohort metrics
are benchmark values, not deployment-prevalence estimates.
Keywords—Quantum Machine Learning; Quantum Feature
Map; Entanglement; Expressibility; Quantum Kernel Methods;
Financial Fraud Detection; Network Intrusion Detection; Hybrid
Quantum-Classical Models
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