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.

翻译：随着量子计算机处理效率的提升和可用性的增强，新兴量子计算平台的应用场景正变得具有经济价值。本研究评估了量子计算辅助的受限玻尔兹曼机（RBM）在信用卡欺诈检测任务中的性能，实验采用巴西领先金融科技公司Stone提供的包含1.45亿笔交易的真实数据集，并在真实量子硬件及模拟器上运行。结果表明，即使使用当前存在噪声的量子退火器，量子辅助RBM方法在多数评价指标上仍能取得优于经典方法的性能。我们的研究为在金融系统中实施量子辅助RBM进行通用故障检测铺平了道路。