用量子熵计算加速金融欺诈检测，训练速度远超传统模型。
Financial Fraud Detection with Entropy Computing
- 基于量子熵计算框架，设计新型分类算法CVQBoost
- 在100万至7000万样本数据上，训练速度显著提升，AUC保持竞争力
- 适合高维、大规模机器学习任务，尤其适用于实时欺诈检测
我们提出CVQBoost，一种利用量子计算公司熵量子计算（EQC）范式及Dirac-3硬件实现的新型分类算法。将其应用于金融欺诈检测场景，并与广泛使用的XGBoost进行对比。实验在最高达48个CPU和4块NVIDIA L4 GPU的高性能硬件上，通过RAPIDS AI框架运行。结果表明，相较于传统方法，CVQBoost在保持相近准确率（以AUC衡量）的同时，在数据集规模和特征复杂度增加时展现出显著更短的训练时间。进一步扩展至100万至7000万样本的合成数据集，验证了其在大规模分类任务中的优异可扩展性。这些发现表明，CVQBoost有望成为梯度提升方法的有力替代方案，为高维机器学习应用如金融欺诈检测提供更高的效率与可扩展性。
原文摘要 · Abstract (English)
We introduce CVQBoost, a novel classification algorithm that leverages early hardware implementing Quantum Computing Inc's Entropy Quantum Computing (EQC) paradigm, Dirac-3 [Nguyen et. al. arXiv:2407.04512]. We apply CVQBoost to a fraud detection test case and benchmark its performance against XGBoost, a widely utilized ML method. Running on Dirac-3, CVQBoost demonstrates a significant runtime advantage over XGBoost, which we evaluate on high-performance hardware comprising up to 48 CPUs and four NVIDIA L4 GPUs using the RAPIDS AI framework. Our results show that CVQBoost maintains competitive accuracy (measured by AUC) while significantly reducing training time, particularly as dataset size and feature complexity increase. To assess scalability, we extend our study to large synthetic datasets ranging from 1M to 70M samples, demonstrating that CVQBoost on Dirac-3 is well-suited for large-scale classification tasks. These findings position CVQBoost as a promising alternative to gradient boosting methods, offering superior scalability and efficiency for high-dimensional ML applications such as fraud detection.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。