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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2512.17660 |
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| _version_ | 1866911328094912512 |
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| author | Neto, João Marcos Cavalcanti de Albuquerque Amaral, Gustavo Castro do Temporão, Guilherme Penello |
| author_facet | Neto, João Marcos Cavalcanti de Albuquerque Amaral, Gustavo Castro do Temporão, Guilherme Penello |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_17660 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Fraud detection in credit card transactions using Quantum-Assisted Restricted Boltzmann Machines Neto, João Marcos Cavalcanti de Albuquerque Amaral, Gustavo Castro do Temporão, Guilherme Penello Quantum Physics Machine Learning 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. |
| title | Fraud detection in credit card transactions using Quantum-Assisted Restricted Boltzmann Machines |
| topic | Quantum Physics Machine Learning |
| url | https://arxiv.org/abs/2512.17660 |