# Fraud detection in credit card transactions using Quantum-Assisted Restricted Boltzmann Machines

arXiv: https://arxiv.org/abs/2512.17660 · HTML: https://mlsanity.com/paper/2512.17660/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 40 references that are in the map.
- First posted on arXiv 19 Dec 2025.

## Builds on (most cited first, 5 of 5)

- [Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation](https://mlsanity.com/paper/2010.16061/) (2020)
- [Application of Quantum Annealing to Training of Deep Neural Networks](https://mlsanity.com/paper/1510.06356/) (2015)
- [Gaussian-Bernoulli RBMs Without Tears](https://mlsanity.com/paper/2210.10318/) (2022)
- [Quantum Computing Assisted Deep Learning for Fault Detection and Diagnosis in Industrial Process Systems](https://mlsanity.com/paper/2003.00264/) (2020)
- [QUBO.jl: A Julia Ecosystem for Quadratic Unconstrained Binary Optimization](https://mlsanity.com/paper/2307.02577/) (2023)

## Abstract (arXiv)

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.