This research aims to assess the effectiveness of quantum kernel-enhanced support vector machines for detecting financial fraud compared to classical methods.
Utilized quantum kernel methods for support vector machines on NISQ simulators
Applied classical baseline methods including PCA and LDA
Evaluated kernel alignment scores using depolarizing noise models.
Quantum kernel methods improved detection rates over classical methods
Notably higher kernel alignment scores were achieved
Demonstrated robustness against depolarizing noise.