Quantum Kernel-Enhanced Support Vector Machines for Financial Fraud Detection: A Benchmarking Study on NISQ Simulators Against Classical Baselines

Charan Panthangi

doi:10.5281/zenodo.20094652

May 9, 2026Open Access

Quantum Kernel-Enhanced Support Vector Machines for Financial Fraud Detection: A Benchmarking Study on NISQ Simulators Against Classical Baselines

View Full Paper

Ask AI

Bookmark

Share

Key Points

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.

Implication

Benchmarking quantum kernel-enhanced support vector machines for financial fraud detection, suggesting new avenues in machine learning.

Abstract

quantum machine learning, quantum kernel methods, financial fraud detection, support vector machine, NISQ, kernel alignment score, ZZFeatureMap, PCA, LDA, depolarizing noise

Discussion

No takes yet. Share an insight, caveat, or question.

Cite This Study

Charan Panthangi (2026) studied this question.

synapsesocial.com/papers/6a0171ed3a9f334c28271f86 https://doi.org/10.5281/zenodo.20094652

View Full Paper

Ask AI

Bookmark

Share

Quantum Kernel-Enhanced Support Vector Machines for Financial Fraud Detection: A Benchmarking Study on NISQ Simulators Against Classical Baselines

Key Points

Implication

Abstract

Discussion

Cite This Study

Quantum Kernel-Enhanced Support Vector Machines for Financial Fraud Detection: A Benchmarking Study on NISQ Simulators Against Classical Baselines

Key Points

Implication

Abstract

Discussion

Cite This Study

Keep reading

Keep reading