用量子辅助玻尔兹曼机检测信用卡欺诈，效果优于传统方法。
Fraud detection in credit card transactions using Quantum-Assisted Restricted Boltzmann Machines
- 用真实量子硬件和模拟器运行量子辅助玻尔兹曼机。
- 在1.45亿条交易数据上，多项指标优于经典方法。
- 适合关注金融系统故障检测的量子计算研究者。
随着量子计算机处理效率提升和可用性增加，新兴量子计算平台的应用逐渐具备经济价值。本研究评估了基于真实量子硬件与模拟器的量子辅助受限玻尔兹曼机（Quantum-Assisted RBM）在信用卡欺诈检测中的表现，使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。结果表明，即便在当前存在噪声的量子退火设备上，量子辅助RBM在多数评价指标上仍优于经典方法。该研究为在金融系统中推广量子辅助RBM进行通用故障检测提供了可行路径。
原文摘要 · Abstract (English)
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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