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Fraud Detection in Credit Card Transactions Using Quantum-Assisted Restricted Boltzmann Machines

Joao Marcos Cavalcanti de Albuquerque Neto et al · IEEE · 2026

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Author / responsible party
Joao Marcos Cavalcanti de Albuquerque Neto et al
Publisher
IEEE
Year
2026
ISSN
2169-3536
ISSN
2169-3536
Language
English
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Use cases for emerging quantum computing platforms become economically relevant as the efficiency of processing and availability of quantum computers increase. We assess the performance of Restricted Boltzmann Machines (RBM) assisted by quantum computing, running on real quantum hardware and simulators, using a real dataset containing 145 million transactions provided by Stone, a leading Brazilian fintech, for credit card fraud detection. The results suggest that the quantum-assisted RBM method is able to achieve superior performance in most figures of merit in comparison to classical approaches, even using current noisy quantum annealers. Our study paves the way for implementing quantum-assisted RBMs for general fault detection in financial systems.

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APA 7

al, J. M. C. D. A. N. E. (2026). Fraud Detection in Credit Card Transactions Using Quantum-Assisted Restricted Boltzmann Machines. https://doi.org/10.1109/ACCESS.2026.3683308

MLA

al, Joao Marcos Cavalcanti de Albuquerque Neto et. "Fraud Detection in Credit Card Transactions Using Quantum-Assisted Restricted Boltzmann Machines." 2026. https://doi.org/10.1109/ACCESS.2026.3683308.

Chicago

al, Joao Marcos Cavalcanti de Albuquerque Neto et. 2026. "Fraud Detection in Credit Card Transactions Using Quantum-Assisted Restricted Boltzmann Machines.". https://doi.org/10.1109/ACCESS.2026.3683308.

Harvard

al, J. M. C. D. A. N. E. 2026, Fraud Detection in Credit Card Transactions Using Quantum-Assisted Restricted Boltzmann Machines, IEEE, available at: https://doi.org/10.1109/ACCESS.2026.3683308 [Accessed 8 Aug. 2026].

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Title
Fraud Detection in Credit Card Transactions Using Quantum-Assisted Restricted Boltzmann Machines
Author / contributors
Joao Marcos Cavalcanti de Albuquerque Neto et al
Publisher
IEEE
Publication year
2026
ISSN
2169-3536
ISSN
2169-3536
Language
English

Subjects

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Quantum computing; quantum annealing; QUBO; restricted Boltzmann machines; fraud detection; quantum machine learning
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