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.

翻译：我们提出CVQBoost，一种利用早期硬件实现Quantum Computing Inc公司熵量子计算范式Dirac-3 [Nguyen et. al. arXiv:2407.04512]的新型分类算法。我们将CVQBoost应用于欺诈检测测试案例，并以广泛使用的机器学习方法XGBoost为基准评估其性能。在Dirac-3上运行时，CVQBoost展现出显著优于XGBoost的运行时间优势——我们使用包含多达48个CPU和四个NVIDIA L4 GPU的高性能硬件，并依托RAPIDS AI框架进行评估。实验结果表明，CVQBoost在保持竞争力准确率（以AUC衡量）的同时，能显著缩短训练时间，这一优势在数据集规模和特征复杂度增加时尤为明显。为评估可扩展性，我们将研究拓展至包含100万至7000万样本的大型合成数据集，证明运行于Dirac-3的CVQBoost非常适合大规模分类任务。这些发现确立了CVQBoost作为梯度提升方法的有力替代方案，为欺诈检测等高维机器学习应用提供了卓越的可扩展性与计算效率。